pax_global_header00006660000000000000000000000064144567760160014532gustar00rootroot0000000000000052 comment=b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/000077500000000000000000000000001445677601600204735ustar00rootroot00000000000000baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/.pylintrc000066400000000000000000000400651445677601600223450ustar00rootroot00000000000000[MASTER] # A comma-separated list of package or module names from where C extensions may # be loaded. Extensions are loading into the active Python interpreter and may # run arbitrary code extension-pkg-whitelist= # Add files or directories to the blacklist. They should be base names, not # paths. ignore=CVS # Add files or directories matching the regex patterns to the blacklist. 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Defaults to # "Exception" overgeneral-exceptions=Exception baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/LICENSE000066400000000000000000000021021445677601600214730ustar00rootroot00000000000000Copyright (c) 2018 Janez Demsar, Alessio Benavoli, Giorgio Corani Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/README.md000066400000000000000000000052371445677601600217610ustar00rootroot00000000000000baycomp ======= Baycomp is a library for Bayesian comparison of classifiers. Functions compare two classifiers on one or on multiple data sets. They compute three probabilities: the probability that the first classifier has higher scores than the second, the probability that differences are within the region of practical equivalence (rope), or that the second classifier has higher scores. We will refer to this probabilities as `p_left`, `p_rope` and `p_right`. If the argument `rope` is omitted (or set to zero), functions return only `p_left` and `p_right`. The region of practical equivalence (rope) is specified by the caller and should correspond to what is "equivalent" in practice; for instance, classification accuracies that differ by less than 0.5 may be called equivalent. Similarly, whether higher scores are better or worse depends upon the type of the score. The library can also plot the posterior distributions. The library can be used in three ways. 1. Two shortcut functions can be used for comparison on single and on multiple data sets. If `nbc` and `j48` contain a list of average classification accuracies of naive Bayesian classifier and J48 on a collection of data sets, we can call >>> two_on_multiple(nbc, j48, rope=1) (0.23124, 0.00666, 0.7621) (Actual results may differ due to Monte Carlo sampling.) With some additional arguments, the function can also plot the posterior distribution from which these probabilities came. 2. Tests are packed into test classes. The above call is equivalent to >>> SignedRankTest.probs(nbc, j48, rope=1) (0.23124, 0.00666, 0.7621) and to get a plot, we call >>> SignedRankTest.plot(nbc, j48, rope=1, names=("nbc", "j48")) To switch to another test, use another class:: >>> SignTest.probs(nbc, j48, rope=1) (0.26508, 0.13274, 0.60218) 3. Finally, we can construct and query sampled posterior distributions. >>> posterior = SignedRankTest(nbc, j48, rope=0.5) >>> posterior.probs() (0.23124, 0.00666, 0.7621) >>> posterior.plot(names=("nbc", "j48")) Installation ------------ Install from [PyPI](https://pypi.python.org/pypi/baycomp): pip install baycomp Documentation ------------- User documentation is available on [https://baycomp.readthedocs.io/](https://baycomp.readthedocs.io/). A detailed description of the implemented methods is available in [Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis](http://jmlr.org/papers/volume18/16-305/16-305.pdf), Alessio Benavoli, Giorgio Corani, Janez DemÅ¡ar, Marco Zaffalon. Journal of Machine Learning Research, 18 (2017) 1-36. baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/baycomp/000077500000000000000000000000001445677601600221255ustar00rootroot00000000000000baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/baycomp/__init__.py000066400000000000000000000001211445677601600242300ustar00rootroot00000000000000# pylint: disable=wildcard-import from .single import * from .multiple import * baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/baycomp/hierarchical-t-test.stan000066400000000000000000000073151445677601600266560ustar00rootroot00000000000000/*Hierarchical Bayesian model for the analysis of competing cross-validated classifiers on multiple data sets. */ data { real deltaLow; real deltaHi; //bounds of the sigma of the higher-level distribution real std0Low; real std0Hi; //bounds on the domain of the sigma of each data set real stdLow; real stdHi; //number of results for each data set. Typically 100 (10 runs of 10-folds cv) int Nsamples; //number of data sets. int q; //difference of accuracy between the two classifier, on each fold of each data set. matrix[q,Nsamples] x; //correlation (1/(number of folds)) real rho; real upperAlpha; real lowerAlpha; real upperBeta; real lowerBeta; } transformed data { //vector of 1s appearing in the likelihood vector[Nsamples] H; //vector of 0s: the mean of the mvn noise vector[Nsamples] zeroMeanVec; /* M is the correlation matrix of the mvn noise. invM is its inverse, detM its determinant */ matrix[Nsamples,Nsamples] invM; real detM; //The determinant of M is analytically known detM = (1+(Nsamples-1)*rho)*(1-rho)^(Nsamples-1); //build H and invM. They do not depend on the data. for (j in 1:Nsamples){ zeroMeanVec[j] = 0; H[j] = 1; for (i in 1:Nsamples){ if (j==i) invM[j,i] = (1 + (Nsamples-2)*rho)*pow((1-rho),Nsamples-2); else invM[j,i] = -rho * pow((1-rho),Nsamples-2); } } /*at this point invM contains the adjugate of M. we divide it by det(M) to obtain the inverse of M.*/ invM = invM/detM; } parameters { //mean of the hyperprior from which we sample the delta_i real delta0; //std of the hyperprior from which we sample the delta_i real std0; //delta_i of each data set: vector of lenght q. vector[q] delta; //sigma of each data set: : vector of lenght q. vector[q] sigma; /* the domain of (nu - 1) starts from 0 and can be given a gamma prior*/ real nuMinusOne; //parameters of the Gamma prior on nuMinusOne real gammaAlpha; real gammaBeta; } transformed parameters { //degrees of freedom real nu ; /*difference between the data (x matrix) and the vector of the q means.*/ matrix[q,Nsamples] diff; vector[q] diagQuad; /*vector of length q: 1 over the variance of each data set*/ vector[q] oneOverSigma2; vector[q] logDetSigma; vector[q] logLik; //degrees of freedom nu = nuMinusOne + 1 ; //1 over the variance of each data set oneOverSigma2 = rep_vector(1, q) ./ sigma; oneOverSigma2 = oneOverSigma2 ./ sigma; /*the data (x) minus a matrix done as follows: the delta vector (of lenght q) pasted side by side Nsamples times*/ diff = x - rep_matrix(delta,Nsamples); //efficient matrix computation of the likelihood. diagQuad = diagonal (quad_form (invM,diff')); logDetSigma = 2*Nsamples*log(sigma) + log(detM) ; logLik = -0.5 * logDetSigma - 0.5*Nsamples*log(6.283); logLik = logLik - 0.5 * oneOverSigma2 .* diagQuad; } model { /*mu0 and std0 are not explicitly sampled here. Stan automatically samples them: mu0 as uniform and std0 as uniform over its domain (std0Low,std0Hi).*/ //sampling the degrees of freedom nuMinusOne ~ gamma ( gammaAlpha, gammaBeta); //vectorial sampling of the delta_i of each data set delta ~ student_t(nu, delta0, std0); //logLik is computed in the previous block target += sum(logLik); } baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/baycomp/multiple.py000066400000000000000000000443271445677601600243440ustar00rootroot00000000000000import os import pickle import numpy as np from scipy import stats from .utils import check_args, seaborn_plt, call_shortcut __all__ = ["SignTest", "SignedRankTest", "HierarchicalTest", "two_on_multiple"] class Posterior: """ Sampled posterior distribution Args: sample (np.array): a 3 x `nsamples` array names (tuple of str or None): names of learning algorithms (default: `None`) """ def __init__(self, sample, *, names=None): self.sample = sample self.names = names def probs(self, with_rope=True): """ Compute and return probabilities Args: with_rope (bool): tells whether the sample includes the probabilities for the rope region (default: `True`) Returns: `(p_left, p_rope, p_right)` if `with_rope=True`; otherwise `(p_left, p_right)`. """ winners = np.argmax(self.sample, axis=1) pl, pe, pr = np.bincount(winners, minlength=3) / len(winners) return (pl, pe, pr) if with_rope else (pl / (pl + pr), pr / (pl + pr)) def plot(self, names=None): """ Plot the posterior distribution. If there are samples in which the probability of `rope` is higher than 0.1, the distribution is shown in a simplex (see :obj:`plot_simplex`), otherwise as a histogram (:obj:`plot_histogram`). Args: names (tuple of str or `None`): names of classifiers Returns: matplotlib figure """ if np.max(self.sample[:, 1]) < 0.1: return self.plot_histogram(names) else: return self.plot_simplex(names) def plot_simplex(self, names=None): """ Plot the posterior distribution in a simplex. The distribution is shown as a triangle with regions corresponding to first classifier having higher scores than the other by more than rope, the second having higher scores, or the difference being within the rope. Args: names (tuple of str): names of classifiers Returns: matplotlib figure """ with seaborn_plt() as plt: from matplotlib.lines import Line2D def project(points): from math import sqrt, sin, cos, pi p1, p2, p3 = points.T / sqrt(3) x = (p2 - p1) * cos(pi / 6) + 0.5 y = p3 - (p1 + p2) * sin(pi / 6) + 1 / (2 * sqrt(3)) return np.vstack((x, y)).T fig, ax = plt.subplots() ax.set_aspect('equal', 'box') # triangle ax.add_line(Line2D([0, 0.5, 1.0, 0], [0, np.sqrt(3) / 2, 0, 0], color='orange')) names = names or self.names or ("C1", "C2") pl, pe, pr = self.probs() ax.text(0, -0.04, 'p({}) = {:.3f}'.format(names[0], pl), horizontalalignment='center', verticalalignment='top') ax.text(0.5, np.sqrt(3) / 2, 'p(rope) = {:.3f}'.format(pe), horizontalalignment='center', verticalalignment='bottom') ax.text(1, -0.04, 'p({}) = {:.3f}'.format(names[1], pr), horizontalalignment='center', verticalalignment='top') cx, cy = project(np.array([[0.3333, 0.3333, 0.3333]]))[0] for x, y in project(np.array([[.5, .5, 0], [.5, 0, .5], [0, .5, .5]])): ax.add_line(Line2D([cx, x], [cy, y], color='orange')) # project and draw points tripts = project(self.sample[:, [0, 2, 1]]) plt.hexbin(tripts[:, 0], tripts[:, 1], mincnt=1, cmap=plt.cm.Blues_r) # Leave some padding around the triangle for vertex labels ax.set_xlim(-0.2, 1.2) ax.set_ylim(-0.2, 1.2) ax.axis('off') return fig def plot_histogram(self, names): """ Plot the posterior distribution as histogram. Args: names (tuple of str): names of classifiers Returns: matplotlib figure """ with seaborn_plt() as plt: names = names or self.names or ("C1", "C2") points = self.sample[:, 2] pr = (np.sum(points > 0.5) + 0.5 * np.sum(points == 0.5)) \ / len(points) pl = 1 - pr fig, ax = plt.subplots() ax.grid(True) ax.hist(points, 50, color="#34ccff") ax.axis(xmin=0, xmax=1) ax.text(0, 0, "\n\np({}) = {:.3f}".format(names[0], pl), horizontalalignment='left', verticalalignment='top') ax.text(1, 0, "\n\np({}) = {:.3f}".format(names[1], pr), horizontalalignment='right', verticalalignment='top') ax.get_yaxis().set_ticklabels([]) ax.axvline(x=0.5, color="#ffad2f", linewidth=2) return fig class Test: LEFT, ROPE, RIGHT = range(3) def __new__(cls, x, y, rope=0, *, nsamples=50000, random_state=None, **kwargs): return Posterior(cls.sample(x, y, rope, nsamples=nsamples, random_state=random_state, **kwargs)) @classmethod def sample(cls, x, y, rope=0, nsamples=50000, **kwargs): """ Compute a sample of posterior distribution. Derived classes override this method to implement specific sampling methods. Derived methods may have additional arguments. Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model rope (float): the width of the region of practical equivalence (default: 0) nsamples (int): the number of samples (default: 50000) Returns: np.array of shape (`nsamples`, 3) """ @classmethod def probs(cls, x, y, rope=0, *, nsamples=50000, **kwargs): """ Compute and return probabilities Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model rope (float): the width of the region of practical equivalence (default: 0) nsamples (int): the number of samples (default: 50000) Returns: `(p_left, p_rope, p_right)` if `rope > 0`; otherwise `(p_left, p_right)`. """ # new returns an instance of Posterior, not Test # pylint: disable=no-value-for-parameter return cls(x, y, rope, nsamples=nsamples, **kwargs).probs(rope > 0) @classmethod def plot(cls, x, y, rope, *, nsamples=50000, names=None, **kwargs): """ Plot the posterior distribution. If there are samples in which the probability of `rope` is higher than 0.1, the distribution is shown in a simplex (see :obj:`plot_simplex`), otherwise as a histogram (:obj:`plot_histogram`). Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model rope (float): the width of the region of practical equivalence (default: 0) nsamples (int): the number of samples (default: 50000) names (tuple of str): names of classifiers Returns: matplotlib figure """ # pylint: disable=no-value-for-parameter return cls(x, y, rope, nsamples=nsamples, **kwargs).plot(names) @classmethod def plot_simplex(cls, x, y, rope, *, nsamples=50000, names=None, **kwargs): """ Plot the posterior distribution in a simplex. The distribution is shown as a triangle with regions corresponding to first classifier having higher scores than the other by more than rope, the second having higher scores, or the difference being within the rope. Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model nsamples (int): the number of samples (default: 50000) names (tuple of str): names of classifiers Returns: matplotlib figure """ # pylint: disable=no-value-for-parameter return cls(x, y, rope, nsamples=nsamples, **kwargs).plot_simplex(names) @classmethod def plot_histogram(cls, x, y, *, nsamples=50000, names=None, **kwargs): """ Plot the posterior distribution as histogram. Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model nsamples (int): the number of samples (default: 50000) names (tuple of str): names of classifiers Returns: matplotlib figure """ # pylint: disable=no-value-for-parameter return cls(x, y, rope=0, nsamples=nsamples, **kwargs)\ .plot_histogram(names) class SignTest(Test): """ Compute a Bayesian sign test (`A Bayesian Wilcoxon signed-rank test based on the Dirichlet process `_, A. Benavoli et al, ICML 2014). Argument `prior` can give a strength (as `float`) of a prior put on the rope region, or a tuple with prior's position and strength, for instance `(SignTest.LEFT, 1.0)`. Position can be `SignTest.LEFT`, `SignTest.ROPE` or `SignTest.RIGHT`. """ @classmethod # pylint: disable=arguments-differ def sample(cls, x, y, rope=0, *, prior=1, nsamples=50000, random_state=None): if isinstance(prior, tuple): prior, prior_place = prior else: prior_place = cls.ROPE check_args(x, y, rope, prior, nsamples) _random_state = np.random.RandomState(random_state) diff = y - x nleft = sum(diff < -rope) nright = sum(diff > rope) nrope = len(diff) - nleft - nright alpha = np.array([nleft, nrope, nright], dtype=float) alpha += 0.0001 # for numerical stability alpha[prior_place] += prior return _random_state.dirichlet(alpha, nsamples) class SignedRankTest(Test): """ Compute a Bayesian signed-rank test (`A Bayesian Wilcoxon signed-rank test based on the Dirichlet process `_, A. Benavoli et al, ICML 2014). Arguments `x` and `y` should be one-dimensional arrays with average performances across data sets. These can be obtained using any sampling method, not necessarily cross validation. """ @classmethod # pylint: disable=arguments-differ def sample(cls, x, y, rope=0, *, prior=0.5, nsamples=50000, random_state=None): def heaviside(a, thresh): return (a > thresh).astype(float) + 0.5 * (a == thresh).astype(float) def diff_sums(x, y): diff = np.hstack(([0], y - x)) diff_m = np.lib.stride_tricks.as_strided( diff, strides=diff.strides + (0,), shape=diff.shape * 2) weights = np.ones(len(diff)) weights[0] = prior return diff_m + diff_m.T, weights def with_rope(): sums, weights = diff_sums(x, y) above_rope = heaviside(sums, 2 * rope) below_rope = heaviside(-sums, 2 * rope) samples = np.zeros((nsamples, 3)) _random_state = np.random.RandomState(random_state) for i, samp_weights in enumerate(_random_state.dirichlet(weights, nsamples)): prod_weights = np.outer(samp_weights, samp_weights) samples[i, 0] = np.sum(prod_weights * below_rope) samples[i, 2] = np.sum(prod_weights * above_rope) samples[:, 1] = -samples[:, 0] - samples[:, 2] + 1 return samples def without_rope(): sums, weights = diff_sums(x, y) above_0 = heaviside(sums, 0) samples = np.zeros((nsamples, 3)) _random_state = np.random.RandomState(random_state) for i, samp_weights in enumerate(_random_state.dirichlet(weights, nsamples)): prod_weights = np.outer(samp_weights, samp_weights) samples[i, 2] = np.sum(prod_weights * above_0) samples[:, 0] = -samples[:, 2] + 1 return samples check_args(x, y, rope, nsamples) return with_rope() if rope > 0 else without_rope() class HierarchicalTest(Test): """ Compute a hierarchical t test. (`Statistical comparison of classifiers through Bayesian hierarchical modelling `_, G. Corani et al, Machine Learning, 2017). Arguments `x` and `y` should be two-dimensional arrays; rows correspond to data sets and elements within rows are scores obtained by (possibly repeated) cross-validation(s). """ @classmethod # pylint: disable=arguments-differ # pylint: disable=unused-argument def sample(cls, x, y, rope, *, runs=1, lower_alpha=1, upper_alpha=2, lower_beta=0.01, upper_beta=0.1, upper_sigma=1000, chains=4, nsamples=10000, random_state=None): try: import stan except ImportError: raise ImportError("Hierarchical model requires 'pystan >= 3.4.0'; " "install it e.g. by 'pip install pystan==3.4.0'") _random_state = np.random.RandomState(random_state) LAST_SAMPLE_PICKLE = "last-sample.pickle" stan_file = os.path.join(os.path.split(__file__)[0], "hierarchical-t-test.stan") args_signature = ( hash(tuple(tuple(e for e in row) for row in y - x)), runs, lower_alpha, upper_alpha, lower_beta, upper_beta, upper_sigma) def try_unpickle(fname): if os.path.exists(fname) \ and os.path.getmtime(fname) > os.path.getmtime(stan_file): with open(fname, "rb") as f: return pickle.load(f) return None def try_pickle(obj, fname): try: with open(fname, "wb") as f: pickle.dump(obj, f) except PermissionError: pass def scaled_data(x, y, rope): # ensure homogenous scale across all data seta diff = y - x stds = np.std(diff, axis=1) std_diff = np.mean(stds) return rope / std_diff, diff / std_diff def prepare_stan_data(diff): ndatasets, nscores = diff.shape nfolds = nscores / runs # avoid numerical problems with zero variance nscores_2 = nscores // 2 for sample in diff: if np.var(sample) == 0: sample[:nscores_2] = _random_state.uniform(-rope, rope, nscores_2) sample[nscores_2:] = -sample[:nscores_2] std_within = np.mean(np.std(diff, axis=1)) # may be different from std_diff! std_among = np.std(np.mean(diff, axis=1)) if ndatasets > 1 else std_within maxdiff = np.max(np.abs(diff)) return dict( x=diff, Nsamples=nscores, q=ndatasets, rho=1 / nfolds, deltaLow=-maxdiff, deltaHi=maxdiff, lowerAlpha=lower_alpha, upperAlpha=upper_alpha, lowerBeta=lower_beta, upperBeta=upper_beta, stdLow=0, stdHi=std_within * upper_sigma, std0Low=0, std0Hi=std_among * upper_sigma ) def run_stan(diff, **kwargs): stan_data = prepare_stan_data(diff) # check if the last pickled result can be reused cached = try_unpickle(LAST_SAMPLE_PICKLE) if cached is not None and cached[0] == args_signature: return cached[1] program_code = open(stan_file).read() model: stan.model.Model = stan.build(program_code, data=stan_data) fit: stan.fit.Fit = model.sample(**kwargs) mu = fit["delta0"][0] stdh = fit["std0"][0] nu = fit["nu"][0] try_pickle((args_signature, (mu, stdh, nu)), LAST_SAMPLE_PICKLE) return mu, stdh, nu rope, diff = scaled_data(x, y, rope) mu, stdh, nu = run_stan(diff, num_samples=nsamples, num_chains=chains) samples = np.empty((len(nu), 3)) for mui, std, df, sample_row in zip(mu, stdh, nu, samples): sample_row[2] = 1 - stats.t.cdf(rope, df, mui, std) sample_row[0] = stats.t.cdf(-rope, df, mui, std) sample_row[1] = 1 - sample_row[0] - sample_row[2] return samples def two_on_multiple(x, y, rope=0, *, runs=1, names=None, plot=False, **kwargs): """ Compute probabilities using a Bayesian signed-ranks test (if `x` and `y` or one-dimensional) or a hierarchical (if they are two-dimensions), and, optionally, draw a histogram. The hierarchical test assumes that the classifiers were evaluated using cross validation; argument `runs` gives the number of repetitions of cross-validation. For more details, see :obj:`SignRankTest` and :obj:`HierarchicalTest` Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model rope (float): the width of the region of practical equivalence (default: 0) runs (int): the number of repetitions of cross validation (for hierarhical model) (default: 1) nsamples (int): the number of samples (default: 50000) plot (bool): if `True`, the function also return a histogram (default: False) names (tuple of str): names of classifiers (ignored if `plot` is `False`) random_state (int or None): random seed for drawing the samples, if None a random seed is picked Returns: `(p_left, p_rope, p_right)` if `rope > 0`; otherwise `(p_left, p_right)`. If `plot=True`, the function also returns a matplotlib figure, that is, `((p_left, p_rope, p_right), fig)` """ if x.ndim == 2: test = HierarchicalTest kwargs["runs"] = runs else: test = SignedRankTest return call_shortcut(test, x, y, rope, names=names, plot=plot, **kwargs) baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/baycomp/single.py000066400000000000000000000207521445677601600237660ustar00rootroot00000000000000from functools import lru_cache import numpy as np from scipy import stats from .utils import check_args, seaborn_plt, call_shortcut __all__ = ["CorrelatedTTest", "two_on_single"] class Posterior: """ The posterior distribution of differences on a single data set. Args: mean (float): the mean difference var (float): the variance df (float): degrees of freedom rope (float): rope (default: 0) meanx (float): mean score of the first classifier; shown in a plot meany (float): mean score of the second classifier; shown in a plot names (tuple of str): names of classifiers; shown in a plot nsamples (int): the number of samples; used only in property `sample`, not in computation of probabilities or plotting (default: 50000) """ def __init__(self, mean, var, df, rope=0, meanx=None, meany=None, *, names=None, nsamples=50000): self.meanx = meanx self.meany = meany self.mean = mean self.var = var self.df = df self.rope = rope self.names = names self.nsamples = nsamples @property @lru_cache(1) def sample(self): """ A sample of differences as 1-dimensional array. Like posteriors for comparison on multiple data sets, an instance of this class will always return the same sample. This sample is not used by other methods. """ if self.var == 0: return np.full((self.nsamples, ), self.mean) return self.mean + \ np.sqrt(self.var) * np.random.standard_t(self.df, self.nsamples) def probs(self): """ Compute and return probabilities Probabilities are not computed from a sample posterior but from cumulative Student distribution. Returns: `(p_left, p_rope, p_right)` if `rope > 0`; otherwise `(p_left, p_right)`. """ t_parameters = self.df, self.mean, np.sqrt(self.var) if self.rope == 0: if self.var == 0: pr = (self.mean > 0) + 0.5 * (self.mean == 0) else: pr = 1 - stats.t.cdf(0, *t_parameters) return 1 - pr, pr else: if self.var == 0: pl = float(self.mean < -self.rope) pr = float(self.mean > self.rope) else: pl = stats.t.cdf(-self.rope, *t_parameters) pr = 1 - stats.t.cdf(self.rope, *t_parameters) return pl, 1 - pl - pr, pr def plot(self, names=None): """ Plot the posterior Student distribution as a histogram. Args: names (tuple of str): names of classifiers Returns: matplotlib figure """ with seaborn_plt() as plt: names = names or self.names or ("C1", "C2") fig, ax = plt.subplots() ax.grid(True) label = "difference" if self.meanx is not None and self.meany is not None: label += " ({}: {:.3f}, {}: {:.3f})".format( names[0], self.meanx, names[1], self.meany) ax.set_xlabel(label) ax.get_yaxis().set_ticklabels([]) ax.axvline(x=-self.rope, color="#ffad2f", linewidth=2, label="rope") ax.axvline(x=self.rope, color="#ffad2f", linewidth=2) targs = (self.df, self.mean, np.sqrt(self.var)) xs = np.linspace(min(stats.t.ppf(0.005, *targs), -1.05 * self.rope), max(stats.t.ppf(0.995, *targs), 1.05 * self.rope), 100) ys = stats.t.pdf(xs, *targs) ax.plot(xs, ys, color="#2f56e0", linewidth=2, label="pdf") ax.fill_between(xs, ys, np.zeros(100), color="#34ccff") ax.legend() return fig class CorrelatedTTest: """ Compute and plot a Bayesian correlated t-test """ def __new__(cls, x, y, rope=0, runs=1, *, names=None, nsamples=50000): check_args(x, y, rope) if not int(runs) == runs > 0: raise ValueError('Number of runs must be a positive integer') if len(x) % round(runs) != 0: raise ValueError("Number of measurements is not divisible by number of runs") mean, var, df = cls.compute_statistics(x, y, runs) return Posterior(mean, var, df, rope, np.mean(x), np.mean(y), names=names, nsamples=nsamples) @classmethod def compute_statistics(cls, x, y, runs=1): """ Compute statistics (mean, variance) from the differences. The number of runs is needed to compute the Nadeau-Bengio correction for underestimated variance. Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model runs (int): number of repetitions of cross validation (default: 1) Returns: mean, var, degrees_of_freedom """ diff = y - x n = len(diff) nfolds = n / runs mean = np.mean(diff) var = np.var(diff, ddof=1) var *= 1 / n + 1 / (nfolds - 1) # Nadeau-Bengio's correction return mean, var, n - 1 @classmethod def sample(cls, x, y, runs=1, *, nsamples=50000): """ Return a sample of posterior distribution for the given data Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model runs (int): number of repetitions of cross validation (default: 1) nsamples (int): the number of samples (default: 50000) Returns: mean, var, degrees_of_freedom """ return cls(x, y, runs=runs, nsamples=nsamples).sample @classmethod def probs(cls, x, y, rope=0, runs=1): """ Compute and return probabilities Probabilities are not computed from a sample posterior but from cumulative Student distribution. Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model rope (float): the width of the region of practical equivalence (default: 0) runs (int): number of repetitions of cross validation (default: 1) Returns: `(p_left, p_rope, p_right)` if `rope > 0`; otherwise `(p_left, p_right)`. """ # new returns an instance of Test, not CorrelatedTTest # pylint: disable=no-value-for-parameter return cls(x, y, rope, runs).probs() @classmethod def plot(cls, x, y, rope=0, runs=1, *, names=None): """ Plot the posterior Student distribution as a histogram. Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model rope (float): the width of the region of practical equivalence (default: 0) names (tuple of str): names of classifiers Returns: matplotlib figure """ # new returns an instance of Test, not CorrelatedTTest # pylint: disable=no-value-for-parameter return cls(x, y, rope, runs).plot(names) def two_on_single(x, y, rope=0, runs=1, *, names=None, plot=False): """ Compute probabilities using a Bayesian correlated t-test and, optionally, draw a histogram. The test assumes that the classifiers were evaluated using cross validation. Argument `runs` gives the number of repetitions of cross-validation. For more details, see :obj:`CorrelatedTTest` Args: x (np.array): a vector of scores for the first model y (np.array): a vector of scores for the second model rope (float): the width of the region of practical equivalence (default: 0) runs (int): the number of repetitions of cross validation (default: 1) nsamples (int): the number of samples (default: 50000) plot (bool): if `True`, the function also return a histogram (default: False) names (tuple of str): names of classifiers (ignored if `plot` is `False`) Returns: `(p_left, p_rope, p_right)` if `rope > 0`; otherwise `(p_left, p_right)`. If `plot=True`, the function also returns a matplotlib figure, that is, `((p_left, p_rope, p_right), fig)` """ return call_shortcut(CorrelatedTTest, x, y, rope, plot=plot, names=names, runs=runs) baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/baycomp/utils.py000066400000000000000000000051421445677601600236410ustar00rootroot00000000000000import contextlib def check_args(x, y, rope=0, prior=1, nsamples=50000): if x.ndim != 1: raise ValueError("'x' must be a 1-dimensional array") if y.ndim != 1: raise ValueError("'y' must be a 1-dimensional array") if len(x) != len(y): raise ValueError("'x' and 'y' must be of same length") if rope < 0: raise ValueError('Rope width cannot be negative') if prior < 0: raise ValueError('Prior strength cannot be negative') if not round(nsamples) == nsamples > 0: raise ValueError('Number of samples must be a positive integer') def call_shortcut(test, x, y, rope, *args, plot=False, names=None, **kwargs): sample = test(x, y, rope, *args, **kwargs) if plot: return sample.probs(), sample.plot(names) else: return sample.probs() @contextlib.contextmanager def seaborn_plt(): # Set a Seaborn-like style. See https://github.com/mwaskom/seaborn. try: import matplotlib as mpl except ImportError: raise ImportError("Plotting requires 'matplotlib'; " "use 'pip install matplotlib' to install it") params = { "font.size": 12, "text.color": ".15", "font.sans-serif": ['Arial', 'DejaVu Sans', 'Liberation Sans', 'Bitstream Vera Sans', 'sans-serif'], "legend.fontsize": 11, "axes.labelsize": 12, "axes.labelcolor": ".15", "axes.axisbelow": True, "axes.facecolor": "#EAEAF2", "axes.edgecolor": "white", "axes.linewidth": 1.25, "grid.linewidth": 1, "grid.color": "white", "xtick.labelsize": 11, "xtick.color": ".15", "xtick.major.width": 1.25, "ytick.left": False, "lines.solid_capstyle": "round", "patch.edgecolor": "w", "patch.force_edgecolor": True} orig_params = {k: mpl.rcParams[k] for k in params} mpl.rcParams.update(params) converter = mpl.colors.colorConverter colors = dict(b="#4C72B0", g="#55A868", r="#C44E52", m="#8172B3", y="#CCB974", c="#64B5CD", k=(.1, .1, .1, .1)) colors = {k: converter.to_rgb(v) for k, v in colors.items()} orig_colors = converter.colors.copy() orig_cache = converter.cache.copy() converter.colors.update(colors) converter.cache.update(colors) import matplotlib.pyplot as plt try: yield plt finally: mpl.rcParams.update(orig_params) converter.colors.clear() converter.colors.update(orig_colors) converter.cache.clear() converter.cache.update(orig_cache) baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/000077500000000000000000000000001445677601600214235ustar00rootroot00000000000000baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/Makefile000066400000000000000000000011341445677601600230620ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. 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Let `nbc` and `j48` contain average performances of two methods for a collection of data sets. With shortcut functions, we computed the signed-ranks test with >>> two_on_multiple(nbc, j48, rope=1) (0.23014, 0.00674, 0.76312) This is equivalent to calling `SignedRankTest.probs`: >>> SignedRankTest.probs(nbc, j48, rope=1) (0.23014, 0.00674, 0.76312) We may choose a different test, the `SignTest`: >>> SignTest.probs(nbc, j48, rope=1) (0.26344, 0.13722, 0.59934) To plot the distribution, we call :: >>> fig = SignedRankTest.plot(nbc, j48, rope=1) Or we may prefer to see as a histogram: >>> fig = SignedRankTest.plot_histogram(nbc, j48, names=("nbc", "j48")) .. image:: _static/signedrank-histogram.png :width: 400px Using test classes instead of shortcut functions offers more control and insight in what the tests do. Single data set --------------- The test for comparing two classifiers on a single data set is implemented in class :obj:`CorrelatedTTest`. The class uses a Bayesian interpretation of the t-test (`A Bayesian approach for comparing cross-validated algorithms on multiple data sets `_, G. Corani and A. Benavoli, Mach Learning 2015). The test assumes that the classifiers were evaluated using cross validation. The number of folds is determined from the length of the vector of differences, as `len(diff) / runs`. Computation includes a correction for underestimation of variance due to overlapping training sets (`Inference for the Generalization Error `_, C. Nadeau and Y. Bengio, Mach Learning 2003). .. autoclass:: CorrelatedTTest :members: Multiple data sets ------------------ The library has three tests for comparisons on multiple data sets: a sign test (:obj:`SignTest`), a signed-rank test (:obj:`SignedRankTest`) and a hierarchical t-test (:obj:`HierarchicalTest`). All classes have a common interface but differ in the computation of the posterior distribution. Consequently, some tests have specific additional parameters. Common methods .............. The common behaviour of all tests is defined in the class :obj:`~baycomp.baycomp.Test`. .. autoclass:: baycomp.multiple.Test :members: :member-order: bysource Note that all methods are class methods. Classes are used as nested namespace. As described in the next section, it is impossible to construct an instance of a `Test` or derived classes. Tests ..... .. autoclass:: SignTest .. autoclass:: SignedRankTest .. autoclass:: HierarchicalTest The test is based on following hierarchical probabilistic model: .. math:: \mathbf{x}_{i} & \sim MVN(\mathbf{1} \mu_i,\mathbf{\Sigma_i}), \mu_1 ... \mu_q & \sim t (\mu_0, \sigma_0,\nu), \sigma_1 ... \sigma_q & \sim \mathrm{unif} (0,\bar{\sigma}), \nu & \sim \mathrm{Gamma}(\alpha,\beta), where :math:`q` and :math:`i` are the number of datasets and the number of measurements, respectively, and .. math:: \alpha & \sim \mathrm{unif} (\underline{\alpha},\overline{\alpha}), \beta & \sim \mathrm{unif} (\underline{\beta}, \overline{\beta}), \mu_0 & \sim \mathrm{unif} (-1, 1), \sigma_0 & \sim \mathrm{unif} (0, \overline{\sigma_0}). Parameters :math:`\underline{\alpha}`, :math:`\bar{\alpha}`, :math:`\underline{\beta}`, :math:`\bar{\beta}` and :math:`\bar{\sigma_0}` can be set through keywords arguments. Defaults are `lower_alpha=1`, `upper_alpha=2`, `lower_beta = 0.01`, `upper_beta = 0.1`, `upper_sigma=1000`. baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/code/000077500000000000000000000000001445677601600223355ustar00rootroot00000000000000baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/code/accuracies.txt000066400000000000000000006037361445677601600252170ustar00rootroot00000000000000nbc anneal 94.444 98.889 94.444 98.889 96.667 95.556 98.889 96.667 94.382 95.506 96.667 97.778 96.667 95.556 97.778 95.556 95.556 88.889 87.640 100.000 96.667 95.556 95.556 94.444 94.444 97.778 96.667 97.778 94.382 94.382 95.556 96.667 96.667 94.444 94.444 91.111 97.778 95.556 97.753 94.382 96.667 97.778 93.333 96.667 95.556 97.778 94.444 96.667 100.000 93.258 93.333 95.556 97.778 96.667 94.444 94.444 96.667 93.333 96.629 96.629 96.667 97.778 95.556 100.000 88.889 95.556 96.667 97.778 95.506 97.753 100.000 95.556 95.556 96.667 96.667 96.667 95.556 98.889 94.382 96.629 95.556 92.222 95.556 97.778 95.556 97.778 94.444 96.667 94.382 96.629 95.556 100.000 95.556 92.222 95.556 97.778 97.778 96.667 94.382 97.753 audiology 73.913 69.565 73.913 60.870 69.565 82.609 72.727 77.273 72.727 59.091 60.870 78.261 73.913 73.913 60.870 60.870 77.273 81.818 72.727 68.182 69.565 60.870 69.565 78.261 73.913 69.565 81.818 68.182 77.273 68.182 52.174 73.913 69.565 65.217 73.913 73.913 86.364 68.182 72.727 81.818 69.565 69.565 78.261 69.565 73.913 73.913 72.727 68.182 72.727 63.636 82.609 73.913 73.913 65.217 65.217 73.913 77.273 72.727 68.182 68.182 65.217 69.565 73.913 65.217 69.565 73.913 77.273 68.182 81.818 63.636 65.217 69.565 82.609 69.565 73.913 65.217 72.727 81.818 63.636 68.182 65.217 69.565 69.565 73.913 73.913 69.565 77.273 81.818 68.182 68.182 78.261 69.565 73.913 73.913 56.522 73.913 81.818 72.727 72.727 59.091 cleeland-14 90.323 90.323 87.097 86.667 76.667 86.667 86.667 83.333 70.000 76.667 90.323 87.097 74.194 90.000 86.667 83.333 86.667 83.333 76.667 80.000 83.871 83.871 93.548 80.000 90.000 66.667 76.667 90.000 80.000 80.000 87.097 70.968 93.548 90.000 93.333 73.333 86.667 83.333 76.667 80.000 77.419 83.871 70.968 96.667 73.333 86.667 90.000 90.000 76.667 90.000 77.419 77.419 80.645 80.000 83.333 76.667 80.000 86.667 90.000 90.000 90.323 74.194 70.968 80.000 93.333 86.667 90.000 83.333 83.333 90.000 77.419 90.323 83.871 86.667 100.000 66.667 80.000 76.667 80.000 90.000 77.419 90.323 80.645 73.333 83.333 76.667 80.000 90.000 96.667 80.000 87.097 87.097 77.419 83.333 86.667 76.667 86.667 80.000 86.667 90.000 cmc 51.351 50.676 54.730 59.184 43.537 59.184 48.299 52.381 46.939 44.218 52.027 50.676 49.324 57.823 50.340 51.701 40.816 49.660 46.939 46.259 47.297 46.622 59.459 48.299 47.619 46.939 48.980 44.218 54.422 53.741 52.027 53.378 52.027 53.061 50.340 51.701 56.463 47.619 47.619 46.259 51.351 45.270 48.649 48.980 54.422 50.340 50.340 46.939 49.660 49.660 53.378 45.946 52.703 53.741 56.463 46.259 47.619 47.619 49.660 51.020 53.378 50.676 45.946 51.020 54.422 41.497 57.143 46.259 48.980 51.020 55.405 44.595 43.243 48.299 54.422 50.340 43.537 55.102 44.898 49.660 47.973 46.622 52.027 42.177 51.701 48.299 54.422 55.102 48.299 55.102 53.378 47.297 50.000 49.660 44.898 48.980 55.782 44.218 50.340 48.299 contact-lenses 66.667 66.667 66.667 66.667 50.000 100.000 100.000 100.000 0.000 100.000 66.667 66.667 66.667 100.000 100.000 100.000 50.000 50.000 100.000 100.000 66.667 66.667 66.667 100.000 50.000 50.000 100.000 50.000 100.000 50.000 33.333 66.667 66.667 100.000 100.000 100.000 100.000 50.000 100.000 50.000 66.667 66.667 66.667 33.333 100.000 100.000 100.000 100.000 50.000 50.000 66.667 100.000 66.667 66.667 50.000 100.000 100.000 100.000 100.000 100.000 66.667 66.667 100.000 100.000 100.000 100.000 100.000 50.000 50.000 50.000 66.667 100.000 100.000 33.333 100.000 100.000 50.000 50.000 50.000 50.000 66.667 100.000 100.000 66.667 100.000 50.000 100.000 0.000 100.000 100.000 66.667 33.333 100.000 33.333 100.000 50.000 100.000 100.000 100.000 100.000 credit 84.058 82.609 84.058 88.406 85.507 88.406 92.754 91.304 85.507 79.710 92.754 91.304 84.058 86.957 84.058 88.406 86.957 82.609 85.507 84.058 86.957 89.855 86.957 79.710 89.855 82.609 86.957 82.609 86.957 86.957 81.159 91.304 79.710 85.507 84.058 82.609 91.304 86.957 92.754 85.507 81.159 89.855 88.406 89.855 86.957 84.058 86.957 84.058 86.957 91.304 89.855 78.261 85.507 91.304 84.058 82.609 81.159 89.855 85.507 91.304 86.957 84.058 85.507 86.957 91.304 79.710 89.855 82.609 92.754 84.058 86.957 84.058 88.406 89.855 85.507 84.058 85.507 86.957 94.203 79.710 89.855 91.304 79.710 84.058 86.957 81.159 89.855 89.855 82.609 81.159 91.304 88.406 88.406 88.406 88.406 81.159 82.609 86.957 78.261 84.058 credit 84.058 82.609 84.058 88.406 85.507 88.406 92.754 91.304 85.507 79.710 92.754 91.304 84.058 86.957 84.058 88.406 86.957 82.609 85.507 84.058 86.957 89.855 86.957 79.710 89.855 82.609 86.957 82.609 86.957 86.957 81.159 91.304 79.710 85.507 84.058 82.609 91.304 86.957 92.754 85.507 81.159 89.855 88.406 89.855 86.957 84.058 86.957 84.058 86.957 91.304 89.855 78.261 85.507 91.304 84.058 82.609 81.159 89.855 85.507 91.304 86.957 84.058 85.507 86.957 91.304 79.710 89.855 82.609 92.754 84.058 86.957 84.058 88.406 89.855 85.507 84.058 85.507 86.957 94.203 79.710 89.855 91.304 79.710 84.058 86.957 81.159 89.855 89.855 82.609 81.159 91.304 88.406 88.406 88.406 88.406 81.159 82.609 86.957 78.261 84.058 ecoli 85.294 79.412 85.294 82.353 85.294 82.353 84.848 72.727 78.788 84.848 79.412 85.294 85.294 88.235 73.529 85.294 81.818 78.788 78.788 81.818 79.412 91.176 76.471 91.176 79.412 79.412 81.818 81.818 84.848 90.909 73.529 82.353 82.353 82.353 76.471 79.412 69.697 81.818 87.879 90.909 79.412 85.294 82.353 85.294 88.235 79.412 81.818 75.758 84.848 84.848 82.353 85.294 73.529 82.353 85.294 79.412 78.788 81.818 84.848 84.848 85.294 79.412 85.294 79.412 76.471 82.353 87.879 78.788 81.818 78.788 88.235 76.471 82.353 82.353 88.235 79.412 90.909 72.727 78.788 78.788 79.412 76.471 73.529 82.353 82.353 91.176 78.788 93.939 81.818 78.788 85.294 76.471 82.353 79.412 82.353 85.294 75.758 81.818 81.818 75.758 eucalyptus 48.649 51.351 63.514 40.541 44.595 47.297 58.904 49.315 56.164 50.685 51.351 56.757 55.405 45.946 58.108 54.054 47.945 52.055 52.055 57.534 55.405 52.703 56.757 59.459 54.054 55.405 42.466 53.425 49.315 53.425 47.297 50.000 48.649 48.649 58.108 50.000 64.384 57.534 54.795 53.425 51.351 54.054 47.297 56.757 56.757 58.108 50.685 41.096 54.795 61.644 58.108 56.757 44.595 51.351 55.405 48.649 47.945 69.863 49.315 46.575 58.108 56.757 56.757 48.649 56.757 47.297 52.055 41.096 43.836 64.384 50.000 50.000 55.405 39.189 55.405 51.351 63.014 47.945 65.753 50.685 63.514 52.703 52.703 50.000 50.000 60.811 43.836 45.205 58.904 52.055 60.811 40.541 63.514 54.054 59.459 50.000 53.425 47.945 50.685 47.945 german-credit 70.000 70.000 77.000 79.000 75.000 76.000 75.000 77.000 80.000 81.000 72.000 77.000 74.000 78.000 73.000 80.000 75.000 78.000 74.000 78.000 77.000 71.000 72.000 81.000 74.000 78.000 72.000 75.000 70.000 76.000 77.000 76.000 76.000 74.000 78.000 75.000 73.000 81.000 73.000 73.000 77.000 72.000 71.000 75.000 73.000 75.000 73.000 72.000 80.000 77.000 75.000 72.000 76.000 78.000 74.000 74.000 74.000 80.000 77.000 71.000 75.000 71.000 76.000 75.000 77.000 79.000 77.000 69.000 79.000 69.000 74.000 74.000 76.000 77.000 72.000 75.000 72.000 72.000 76.000 83.000 64.000 83.000 75.000 73.000 73.000 78.000 84.000 73.000 76.000 67.000 76.000 75.000 71.000 68.000 75.000 83.000 72.000 76.000 75.000 72.000 glass 63.636 77.273 59.091 68.182 76.190 85.714 61.905 71.429 76.190 66.667 72.727 81.818 63.636 63.636 76.190 76.190 85.714 61.905 66.667 80.952 81.818 81.818 81.818 68.182 71.429 76.190 71.429 85.714 57.143 71.429 63.636 77.273 68.182 72.727 66.667 76.190 57.143 71.429 76.190 80.952 63.636 59.091 77.273 68.182 80.952 66.667 66.667 85.714 80.952 66.667 68.182 68.182 77.273 72.727 80.952 76.190 52.381 66.667 85.714 66.667 63.636 68.182 77.273 72.727 76.190 80.952 85.714 66.667 80.952 61.905 63.636 72.727 81.818 72.727 71.429 61.905 80.952 61.905 57.143 85.714 72.727 68.182 72.727 77.273 71.429 71.429 71.429 71.429 66.667 71.429 77.273 81.818 68.182 45.455 90.476 76.190 80.952 47.619 76.190 66.667 grub-damage 56.250 50.000 43.750 43.750 37.500 60.000 33.333 66.667 46.667 40.000 50.000 50.000 43.750 43.750 50.000 66.667 53.333 60.000 40.000 60.000 50.000 43.750 56.250 56.250 43.750 66.667 46.667 60.000 40.000 60.000 50.000 62.500 50.000 37.500 37.500 53.333 46.667 53.333 53.333 46.667 62.500 43.750 56.250 56.250 50.000 40.000 26.667 60.000 60.000 53.333 37.500 50.000 43.750 62.500 50.000 20.000 46.667 60.000 73.333 60.000 37.500 43.750 43.750 50.000 43.750 66.667 53.333 40.000 66.667 80.000 25.000 68.750 31.250 62.500 43.750 53.333 60.000 40.000 53.333 53.333 56.250 56.250 56.250 43.750 43.750 53.333 53.333 33.333 60.000 33.333 31.250 62.500 50.000 43.750 50.000 40.000 40.000 66.667 40.000 60.000 haberman 67.742 77.419 77.419 70.968 77.419 74.194 73.333 66.667 70.000 70.000 61.290 74.194 70.968 74.194 77.419 74.194 73.333 70.000 73.333 73.333 74.194 74.194 67.742 70.968 64.516 58.065 73.333 76.667 73.333 73.333 64.516 77.419 77.419 70.968 67.742 67.742 70.000 83.333 80.000 63.333 70.968 74.194 70.968 70.968 70.968 74.194 76.667 66.667 73.333 70.000 74.194 77.419 67.742 67.742 80.645 80.645 70.000 70.000 66.667 66.667 70.968 70.968 77.419 70.968 83.871 67.742 80.000 73.333 63.333 76.667 61.290 70.968 77.419 70.968 70.968 64.516 73.333 76.667 66.667 76.667 74.194 64.516 70.968 67.742 70.968 77.419 70.000 70.000 66.667 80.000 70.968 70.968 70.968 70.968 70.968 74.194 70.000 83.333 73.333 73.333 hayes-roth 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 hepatitis 100.000 93.750 75.000 75.000 81.250 86.667 73.333 86.667 73.333 80.000 81.250 81.250 100.000 75.000 81.250 86.667 100.000 60.000 86.667 86.667 87.500 75.000 93.750 81.250 87.500 80.000 93.333 86.667 73.333 93.333 62.500 87.500 87.500 81.250 62.500 93.333 86.667 86.667 93.333 86.667 93.750 81.250 81.250 100.000 75.000 93.333 80.000 80.000 66.667 86.667 81.250 93.750 68.750 81.250 81.250 86.667 93.333 86.667 86.667 73.333 87.500 93.750 75.000 87.500 87.500 73.333 66.667 93.333 93.333 100.000 87.500 100.000 62.500 75.000 81.250 73.333 80.000 86.667 100.000 86.667 68.750 93.750 75.000 93.750 87.500 86.667 73.333 80.000 80.000 93.333 93.750 87.500 68.750 100.000 75.000 80.000 66.667 93.333 73.333 93.333 hungarian-14 73.333 76.667 90.000 86.667 93.103 89.655 79.310 93.103 82.759 82.759 80.000 86.667 90.000 86.667 89.655 82.759 72.414 86.207 86.207 86.207 80.000 83.333 86.667 83.333 96.552 82.759 86.207 93.103 75.862 75.862 90.000 76.667 83.333 83.333 68.966 89.655 75.862 89.655 93.103 93.103 90.000 80.000 76.667 80.000 93.103 93.103 72.414 79.310 86.207 93.103 83.333 83.333 86.667 86.667 82.759 89.655 75.862 79.310 86.207 86.207 90.000 80.000 86.667 86.667 86.207 86.207 79.310 93.103 75.862 82.759 80.000 76.667 93.333 86.667 79.310 82.759 86.207 82.759 79.310 86.207 83.333 83.333 76.667 90.000 86.207 79.310 82.759 79.310 93.103 93.103 73.333 80.000 86.667 96.667 86.207 86.207 86.207 82.759 75.862 89.655 hypothyroid 97.884 98.148 97.878 99.204 98.143 98.408 99.469 98.674 98.143 97.082 98.413 99.471 97.878 97.347 98.408 97.878 98.143 98.143 98.408 98.408 98.148 98.942 98.939 98.408 98.674 97.347 97.878 97.613 97.878 98.674 98.413 97.354 98.408 98.939 98.143 97.878 98.674 98.143 98.939 98.408 98.677 97.884 97.347 98.674 97.613 98.143 98.674 98.143 99.469 98.674 97.090 98.413 97.878 99.204 99.204 98.143 98.408 97.613 98.408 98.408 99.206 98.413 97.347 98.674 98.143 97.878 98.674 99.204 97.347 98.408 98.148 97.090 99.735 98.939 97.347 98.674 98.143 98.939 98.408 98.674 98.413 96.561 99.735 97.613 99.204 98.939 98.143 98.408 98.408 98.143 99.735 98.148 98.939 97.613 98.674 97.347 98.408 97.878 97.878 98.143 ionosphere 88.889 82.857 88.571 88.571 91.429 94.286 94.286 82.857 91.429 88.571 91.667 80.000 91.429 88.571 88.571 97.143 88.571 94.286 91.429 82.857 83.333 91.429 88.571 88.571 97.143 77.143 94.286 91.429 88.571 91.429 94.444 91.429 88.571 91.429 91.429 88.571 91.429 88.571 85.714 82.857 83.333 94.286 88.571 94.286 85.714 91.429 91.429 82.857 85.714 94.286 86.111 85.714 82.857 88.571 97.143 100.000 94.286 91.429 88.571 82.857 88.889 80.000 88.571 91.429 91.429 88.571 94.286 77.143 94.286 97.143 86.111 85.714 88.571 91.429 85.714 97.143 91.429 94.286 82.857 94.286 91.667 91.429 85.714 91.429 88.571 91.429 85.714 82.857 97.143 94.286 97.222 100.000 88.571 85.714 80.000 85.714 85.714 88.571 88.571 88.571 iris 93.333 100.000 100.000 100.000 93.333 93.333 86.667 86.667 80.000 93.333 93.333 80.000 86.667 100.000 100.000 93.333 100.000 93.333 86.667 100.000 100.000 93.333 93.333 93.333 86.667 100.000 86.667 86.667 93.333 93.333 80.000 93.333 93.333 93.333 100.000 93.333 100.000 100.000 93.333 100.000 100.000 73.333 100.000 100.000 93.333 100.000 93.333 86.667 93.333 93.333 93.333 100.000 93.333 86.667 93.333 93.333 93.333 93.333 100.000 100.000 93.333 86.667 86.667 93.333 93.333 100.000 93.333 93.333 93.333 93.333 86.667 93.333 93.333 100.000 100.000 80.000 100.000 100.000 86.667 86.667 93.333 100.000 86.667 93.333 93.333 100.000 93.333 93.333 93.333 93.333 100.000 93.333 100.000 86.667 93.333 93.333 93.333 80.000 93.333 93.333 kr-s-kp 90.312 84.375 88.125 87.188 88.438 90.312 89.028 87.147 86.207 87.774 87.812 89.688 87.812 85.625 88.750 87.188 85.893 87.147 87.147 90.909 83.438 87.188 85.312 88.125 87.500 88.125 89.655 87.147 89.028 92.163 89.375 85.938 87.188 88.750 86.562 89.375 87.461 88.401 83.072 89.342 88.125 90.938 86.875 85.938 88.438 87.188 87.147 90.909 89.342 84.953 87.188 86.250 88.750 88.438 85.625 89.375 88.715 90.282 89.655 82.759 86.875 85.000 91.250 86.875 89.375 87.188 85.893 87.147 90.909 85.893 89.062 86.875 88.750 86.875 91.250 85.312 89.969 87.147 85.266 87.147 87.812 89.062 91.562 84.062 89.062 88.125 86.520 86.520 89.655 85.580 87.500 90.000 89.375 88.438 89.375 85.938 87.774 85.580 87.461 88.715 labor 66.667 66.667 83.333 66.667 100.000 83.333 83.333 80.000 100.000 100.000 83.333 83.333 100.000 83.333 83.333 100.000 100.000 80.000 100.000 60.000 100.000 83.333 83.333 100.000 100.000 83.333 66.667 100.000 100.000 80.000 100.000 66.667 100.000 83.333 83.333 100.000 66.667 100.000 80.000 80.000 66.667 100.000 83.333 100.000 100.000 66.667 66.667 100.000 100.000 100.000 83.333 83.333 83.333 100.000 83.333 83.333 83.333 100.000 80.000 60.000 83.333 100.000 83.333 83.333 100.000 83.333 83.333 100.000 100.000 80.000 83.333 100.000 100.000 83.333 83.333 100.000 66.667 100.000 80.000 100.000 83.333 83.333 100.000 100.000 100.000 83.333 100.000 60.000 100.000 80.000 100.000 100.000 83.333 66.667 83.333 100.000 100.000 60.000 100.000 100.000 lier-disorders 48.571 57.143 54.286 57.143 57.143 58.824 58.824 55.882 52.941 61.765 57.143 57.143 57.143 57.143 57.143 55.882 55.882 58.824 50.000 58.824 51.429 51.429 62.857 54.286 57.143 58.824 58.824 58.824 55.882 58.824 54.286 62.857 57.143 57.143 62.857 58.824 55.882 58.824 58.824 52.941 51.429 57.143 57.143 60.000 57.143 55.882 58.824 61.765 58.824 50.000 51.429 60.000 42.857 57.143 57.143 58.824 58.824 58.824 50.000 58.824 60.000 57.143 54.286 57.143 57.143 38.235 58.824 55.882 58.824 58.824 54.286 60.000 57.143 57.143 57.143 58.824 61.765 61.765 52.941 58.824 45.714 57.143 51.429 60.000 57.143 58.824 58.824 58.824 61.765 58.824 62.857 62.857 57.143 62.857 48.571 58.824 58.824 50.000 61.765 58.824 lymphography 86.667 93.333 93.333 73.333 86.667 86.667 86.667 80.000 78.571 78.571 93.333 93.333 86.667 73.333 86.667 93.333 66.667 73.333 92.857 85.714 80.000 93.333 93.333 73.333 80.000 80.000 80.000 93.333 92.857 92.857 80.000 73.333 93.333 86.667 80.000 93.333 66.667 93.333 92.857 100.000 80.000 93.333 86.667 86.667 86.667 80.000 86.667 86.667 71.429 92.857 80.000 86.667 86.667 66.667 100.000 86.667 66.667 86.667 85.714 92.857 66.667 80.000 86.667 73.333 100.000 93.333 80.000 93.333 85.714 92.857 73.333 86.667 80.000 86.667 93.333 80.000 86.667 86.667 92.857 71.429 93.333 86.667 93.333 93.333 86.667 93.333 80.000 73.333 78.571 85.714 86.667 80.000 80.000 80.000 86.667 86.667 73.333 100.000 100.000 85.714 monks 55.738 61.667 63.333 61.667 61.667 61.667 63.333 65.000 65.000 63.333 59.016 66.667 65.000 63.333 60.000 61.667 63.333 65.000 60.000 61.667 63.934 60.000 60.000 65.000 58.333 65.000 63.333 63.333 61.667 63.333 63.934 61.667 63.333 61.667 65.000 63.333 65.000 66.667 58.333 66.667 57.377 61.667 63.333 58.333 63.333 65.000 61.667 65.000 65.000 61.667 60.656 63.333 66.667 63.333 66.667 60.000 61.667 63.333 61.667 65.000 62.295 63.333 61.667 63.333 61.667 65.000 63.333 66.667 56.667 61.667 60.656 61.667 65.000 61.667 65.000 65.000 63.333 63.333 61.667 61.667 63.934 65.000 61.667 65.000 60.000 61.667 61.667 61.667 63.333 65.000 60.656 60.000 63.333 61.667 61.667 65.000 65.000 58.333 63.333 61.667 monks1 75.000 71.429 75.000 73.214 76.786 76.786 70.909 76.364 74.545 76.364 85.714 71.429 66.071 76.786 78.571 71.429 76.364 72.727 76.364 70.909 75.000 71.429 76.786 73.214 67.857 73.214 83.636 76.364 72.727 76.364 83.929 76.786 73.214 71.429 76.786 71.429 70.909 78.182 70.909 72.727 75.000 75.000 83.929 78.571 78.571 73.214 78.182 67.273 70.909 65.455 76.786 75.000 73.214 73.214 73.214 75.000 76.364 72.727 70.909 80.000 73.214 73.214 80.357 69.643 75.000 82.143 65.455 76.364 78.182 72.727 75.000 82.143 69.643 69.643 78.571 73.214 70.909 74.545 70.909 81.818 67.857 69.643 82.143 71.429 75.000 75.000 76.364 78.182 74.545 76.364 80.357 71.429 80.357 67.857 76.786 80.357 72.727 76.364 74.545 65.455 monks3 96.429 98.214 92.857 96.429 100.000 98.182 94.545 94.545 100.000 92.727 94.643 94.643 100.000 96.429 96.364 98.182 98.182 92.727 94.545 98.182 100.000 92.857 98.214 89.286 96.364 100.000 96.364 94.545 96.364 100.000 100.000 92.857 98.214 98.214 96.364 92.727 98.182 98.182 94.545 94.545 96.429 100.000 96.429 98.214 92.727 94.545 94.545 94.545 98.182 98.182 100.000 94.643 92.857 98.214 98.182 96.364 98.182 94.545 94.545 96.364 94.643 98.214 98.214 96.429 90.909 98.182 94.545 98.182 98.182 96.364 91.071 98.214 100.000 96.429 94.545 96.364 98.182 96.364 92.727 100.000 94.643 98.214 96.429 94.643 96.364 96.364 92.727 100.000 98.182 96.364 98.214 100.000 94.643 92.857 94.545 98.182 98.182 96.364 98.182 92.727 mushroom 95.818 95.572 96.310 95.326 96.059 95.320 95.813 96.552 95.567 95.936 95.818 95.080 94.957 96.679 96.429 95.567 94.828 97.167 95.443 94.704 95.449 96.556 95.818 96.925 95.567 96.059 95.320 95.320 95.567 95.320 95.941 94.588 95.080 95.695 95.813 95.320 96.059 96.921 95.690 96.552 96.064 95.203 96.064 94.588 95.936 96.552 96.182 95.567 95.443 95.690 95.572 96.187 96.556 95.572 96.429 94.951 96.182 94.951 96.305 95.690 94.711 97.048 96.064 96.187 95.074 96.305 96.675 94.704 95.443 95.074 96.556 95.941 94.465 95.326 96.921 94.458 94.704 96.675 95.813 96.059 95.449 94.834 96.925 97.417 96.429 95.443 95.320 94.704 96.059 94.951 96.064 96.064 94.342 96.556 93.842 94.951 96.552 96.305 96.552 96.921 nursery 90.432 90.046 91.512 89.429 90.432 90.664 89.660 91.358 88.657 91.049 91.975 89.815 89.660 90.201 90.972 90.509 90.278 90.278 89.815 90.201 89.738 90.278 90.432 90.818 89.815 90.201 90.586 89.969 89.969 90.895 91.049 91.898 89.275 91.512 90.201 89.352 89.969 89.892 90.355 89.660 90.201 89.969 90.895 90.972 89.738 91.049 91.358 89.815 89.275 89.198 89.738 89.120 89.969 90.818 90.355 91.358 90.509 90.664 91.435 88.966 91.049 90.818 91.204 89.198 91.744 90.123 89.352 89.429 89.352 90.818 90.664 90.432 89.275 90.201 90.355 90.895 90.432 89.892 90.355 90.278 89.969 89.429 90.818 90.355 91.358 90.972 91.667 89.583 89.275 89.583 90.818 91.127 90.278 90.664 89.660 89.043 90.046 90.355 90.972 89.892 optdigits 91.815 91.993 91.459 92.883 92.527 90.569 93.060 94.128 92.527 91.993 91.993 94.662 93.238 89.680 91.637 93.772 91.815 91.815 90.747 91.815 91.815 93.060 92.171 91.815 93.416 90.391 91.637 91.459 93.416 91.815 92.705 91.103 90.391 92.171 94.306 93.772 92.527 93.060 90.391 91.281 91.815 92.705 92.349 93.950 91.993 92.349 91.459 92.527 90.747 92.705 91.815 91.815 91.281 92.883 92.705 91.281 91.993 92.349 91.815 92.527 91.815 94.306 91.103 91.459 92.349 91.459 92.349 94.840 92.349 91.815 91.637 92.527 92.527 90.925 91.637 92.171 93.238 93.060 90.391 92.883 92.527 91.637 91.815 91.815 93.594 92.527 91.993 90.747 93.416 92.349 93.060 89.146 90.925 91.459 92.705 93.594 92.527 93.416 91.637 91.281 owel 93.978 92.336 93.431 94.150 93.601 93.236 94.516 93.967 93.053 92.870 93.796 92.336 92.518 93.784 94.516 91.590 93.784 94.333 94.698 92.870 91.971 94.526 91.423 93.601 95.978 93.053 92.687 92.505 94.698 93.784 94.343 93.613 94.343 92.322 93.967 93.601 92.870 94.333 92.687 91.408 93.796 94.708 92.701 92.322 93.236 93.236 94.698 94.516 92.505 93.053 95.073 92.153 92.518 92.687 92.505 91.773 95.247 93.967 92.870 94.150 92.518 95.438 92.518 93.601 91.773 94.150 93.419 91.590 95.247 92.870 93.796 94.891 93.248 93.784 93.053 93.419 93.053 92.139 91.408 93.967 92.701 92.883 93.978 92.687 93.236 92.139 93.053 94.150 93.601 95.247 92.153 93.796 91.606 93.053 93.053 93.419 92.687 94.516 95.247 93.601 page-blocks 75.000 75.000 75.000 100.000 100.000 50.000 66.667 66.667 100.000 100.000 75.000 100.000 50.000 75.000 100.000 100.000 100.000 66.667 100.000 66.667 50.000 75.000 100.000 100.000 75.000 75.000 33.333 100.000 100.000 100.000 75.000 100.000 50.000 100.000 75.000 75.000 66.667 100.000 100.000 66.667 100.000 75.000 50.000 75.000 50.000 75.000 100.000 100.000 66.667 100.000 75.000 100.000 75.000 50.000 75.000 100.000 100.000 100.000 66.667 100.000 100.000 75.000 75.000 75.000 100.000 50.000 66.667 66.667 66.667 100.000 50.000 50.000 100.000 75.000 100.000 75.000 66.667 100.000 66.667 100.000 50.000 75.000 75.000 100.000 100.000 75.000 100.000 100.000 66.667 66.667 75.000 50.000 75.000 100.000 75.000 75.000 100.000 33.333 100.000 100.000 pasture-production 88.455 89.091 89.081 85.532 87.898 87.625 88.535 87.261 87.898 86.442 88.727 88.364 87.989 87.261 87.534 88.444 88.626 87.989 85.805 86.806 86.818 87.273 87.625 86.533 88.535 88.171 86.624 88.990 89.445 87.807 86.909 86.091 86.988 89.172 86.624 86.897 88.444 89.900 87.898 87.716 88.455 88.000 87.716 89.354 87.079 86.442 88.899 86.442 86.897 87.807 88.455 89.182 86.806 87.079 87.625 86.442 88.262 86.897 87.443 88.808 86.455 88.636 87.261 87.534 87.807 87.170 87.807 88.808 88.080 86.624 87.455 87.727 86.897 88.990 87.261 88.808 86.078 89.263 86.715 88.808 88.455 87.909 89.627 90.173 87.352 86.169 87.534 86.442 86.169 87.079 87.545 90.364 86.715 86.806 88.171 86.806 87.898 89.354 85.805 87.534 pendigits 74.026 71.429 81.818 76.623 74.026 72.727 66.234 71.429 73.684 81.579 76.623 72.727 76.623 71.429 76.623 80.519 72.727 75.325 80.263 69.737 70.130 70.130 76.623 77.922 83.117 68.831 71.429 75.325 80.263 80.263 80.519 75.325 76.623 70.130 71.429 76.623 77.922 83.117 73.684 71.053 80.519 72.727 80.519 76.623 74.026 74.026 74.026 77.922 67.105 72.368 71.429 74.026 67.532 80.519 71.429 76.623 70.130 85.714 84.211 67.105 70.130 74.026 79.221 68.831 75.325 67.532 84.416 79.221 75.000 78.947 85.714 80.519 72.727 77.922 76.623 72.727 72.727 68.831 65.789 81.579 74.026 79.221 68.831 79.221 64.935 76.623 75.325 84.416 76.316 77.632 76.623 76.623 75.325 75.325 76.623 76.623 77.922 75.325 80.263 65.789 pima-diabetes 66.667 66.667 66.667 66.667 66.667 77.778 66.667 66.667 66.667 55.556 66.667 77.778 77.778 77.778 66.667 66.667 66.667 66.667 66.667 55.556 77.778 88.889 77.778 66.667 55.556 66.667 55.556 66.667 66.667 55.556 66.667 77.778 66.667 77.778 66.667 66.667 77.778 66.667 66.667 55.556 66.667 66.667 66.667 66.667 77.778 77.778 77.778 55.556 55.556 66.667 77.778 66.667 77.778 77.778 55.556 66.667 66.667 66.667 66.667 66.667 77.778 77.778 88.889 66.667 44.444 66.667 66.667 66.667 66.667 66.667 77.778 66.667 77.778 55.556 66.667 66.667 66.667 66.667 66.667 55.556 66.667 55.556 55.556 66.667 66.667 55.556 77.778 77.778 77.778 77.778 55.556 66.667 77.778 66.667 77.778 77.778 66.667 77.778 66.667 55.556 postoperatie 55.882 52.941 47.059 44.118 44.118 47.059 44.118 47.059 44.118 42.424 38.235 41.176 41.176 44.118 52.941 50.000 55.882 41.176 44.118 51.515 50.000 47.059 41.176 55.882 50.000 52.941 52.941 44.118 41.176 48.485 44.118 55.882 35.294 50.000 58.824 47.059 47.059 44.118 47.059 39.394 47.059 50.000 50.000 58.824 44.118 50.000 44.118 47.059 38.235 48.485 44.118 35.294 35.294 50.000 52.941 50.000 38.235 52.941 47.059 57.576 41.176 47.059 50.000 61.765 47.059 47.059 52.941 47.059 38.235 51.515 50.000 47.059 58.824 52.941 41.176 50.000 41.176 41.176 47.059 42.424 44.118 47.059 52.941 52.941 55.882 47.059 35.294 50.000 35.294 48.485 50.000 50.000 61.765 38.235 44.118 52.941 41.176 44.118 47.059 42.424 primary-tumor 90.043 91.775 89.610 90.909 93.074 90.043 89.610 91.775 92.208 93.939 90.476 92.641 90.476 90.043 90.476 91.775 88.745 90.043 93.074 92.641 92.641 95.671 90.909 92.641 90.476 90.909 89.610 90.043 88.312 89.177 92.208 89.610 89.610 93.506 93.074 89.177 93.074 87.446 91.775 92.641 90.476 89.177 90.476 93.506 93.074 90.476 93.074 89.177 91.342 89.610 91.342 90.476 93.074 90.476 93.506 90.476 92.641 89.610 85.714 90.476 89.610 91.775 90.043 91.342 90.909 91.342 93.074 91.342 92.208 89.177 90.909 92.641 90.043 88.745 91.342 87.446 90.909 94.805 91.775 94.805 90.476 91.775 88.745 90.909 89.177 91.342 93.074 91.775 93.506 92.208 91.775 91.775 92.208 88.312 90.476 92.208 89.610 92.208 92.208 92.641 segment 81.818 87.879 84.848 84.375 87.500 90.625 87.500 81.250 87.500 87.500 87.879 87.879 78.788 90.625 90.625 90.625 90.625 81.250 71.875 87.500 87.879 84.848 84.848 75.000 75.000 93.750 90.625 84.375 87.500 84.375 87.879 81.818 72.727 87.500 87.500 84.375 84.375 87.500 90.625 87.500 90.909 78.788 84.848 90.625 87.500 84.375 81.250 84.375 84.375 87.500 81.818 84.848 90.909 81.250 90.625 81.250 96.875 81.250 84.375 84.375 78.788 75.758 84.848 84.375 90.625 84.375 87.500 90.625 93.750 87.500 75.758 81.818 81.818 93.750 84.375 90.625 90.625 81.250 87.500 87.500 84.848 87.879 81.818 87.500 84.375 90.625 84.375 84.375 87.500 84.375 81.818 78.788 84.848 90.625 90.625 90.625 84.375 81.250 90.625 84.375 solar-flare-C 90.909 78.788 90.909 78.125 81.250 90.625 87.500 87.500 96.875 87.500 87.879 90.909 90.909 87.500 84.375 87.500 81.250 84.375 81.250 93.750 87.879 90.909 78.788 90.625 87.500 87.500 87.500 87.500 84.375 90.625 87.879 87.879 87.879 84.375 87.500 87.500 81.250 93.750 90.625 81.250 90.909 90.909 78.788 90.625 90.625 84.375 78.125 90.625 87.500 84.375 90.909 87.879 81.818 84.375 90.625 87.500 87.500 81.250 78.125 87.500 90.909 81.818 87.879 87.500 81.250 87.500 87.500 87.500 84.375 87.500 90.909 81.818 78.788 90.625 84.375 90.625 87.500 93.750 90.625 81.250 78.788 87.879 93.939 78.125 84.375 84.375 84.375 84.375 90.625 93.750 87.879 87.879 87.879 90.625 84.375 84.375 90.625 81.250 78.125 87.500 solar-flare-X 100.000 93.939 90.909 93.750 96.875 90.625 90.625 87.500 90.625 96.875 96.970 78.788 93.939 96.875 93.750 100.000 93.750 96.875 93.750 100.000 90.909 93.939 93.939 87.500 90.625 100.000 93.750 96.875 96.875 93.750 93.939 93.939 96.970 93.750 96.875 96.875 100.000 87.500 87.500 93.750 90.909 90.909 93.939 93.750 100.000 90.625 90.625 93.750 96.875 96.875 93.939 93.939 90.909 90.625 93.750 96.875 96.875 84.375 93.750 100.000 90.909 96.970 93.939 93.750 96.875 90.625 93.750 96.875 90.625 96.875 93.939 96.970 93.939 90.625 93.750 90.625 90.625 93.750 100.000 96.875 100.000 87.879 90.909 90.625 93.750 100.000 87.500 96.875 96.875 90.625 84.848 100.000 90.909 93.750 96.875 96.875 90.625 93.750 96.875 90.625 solar-flare-m 85.714 76.190 80.952 85.714 76.190 66.667 85.714 85.714 80.000 80.000 76.190 71.429 71.429 71.429 66.667 61.905 80.952 76.190 95.000 95.000 66.667 85.714 85.714 90.476 85.714 61.905 61.905 71.429 60.000 80.000 66.667 90.476 71.429 66.667 85.714 71.429 71.429 66.667 95.000 80.000 76.190 66.667 76.190 85.714 66.667 80.952 66.667 80.952 95.000 85.000 90.476 90.476 76.190 66.667 71.429 66.667 85.714 71.429 65.000 65.000 85.714 80.952 61.905 85.714 80.952 90.476 52.381 76.190 85.000 90.000 71.429 85.714 76.190 76.190 71.429 66.667 80.952 85.714 85.000 80.000 76.190 76.190 85.714 80.952 57.143 80.952 66.667 76.190 70.000 80.000 80.952 71.429 85.714 61.905 76.190 66.667 76.190 90.476 60.000 70.000 sonar 94.203 92.754 95.652 91.176 92.647 91.176 92.647 92.647 86.765 91.176 91.304 94.203 88.406 98.529 95.588 97.059 91.176 89.706 85.294 89.706 97.101 92.754 94.203 92.647 92.647 91.176 88.235 97.059 94.118 88.235 92.754 95.652 89.855 94.118 88.235 91.176 94.118 92.647 94.118 91.176 91.304 88.406 91.304 92.647 91.176 85.294 92.647 97.059 92.647 95.588 84.058 97.101 94.203 95.588 91.176 92.647 92.647 91.176 89.706 89.706 89.855 92.754 91.304 94.118 89.706 92.647 91.176 98.529 86.765 95.588 91.304 92.754 97.101 94.118 86.765 98.529 91.176 82.353 94.118 97.059 88.406 86.957 91.304 94.118 97.059 92.647 92.647 89.706 92.647 94.118 94.203 94.203 92.754 95.588 89.706 88.235 94.118 94.118 88.235 91.176 soybean 90.672 89.783 89.565 90.217 89.783 88.261 90.217 90.217 88.478 91.304 90.022 88.261 89.565 90.870 89.348 89.130 91.087 90.217 88.261 89.565 89.588 90.870 88.696 90.000 88.913 90.435 89.783 88.696 89.348 91.304 88.503 89.783 91.957 89.130 88.913 92.391 91.739 89.565 87.826 88.043 89.805 87.609 90.652 89.348 90.217 89.783 91.304 93.043 89.130 89.565 86.985 91.304 90.217 89.348 91.087 89.130 90.435 88.913 88.696 91.304 89.154 90.870 92.609 90.435 88.261 85.652 90.000 90.000 90.652 90.435 88.937 89.783 89.565 89.565 92.609 89.565 88.478 90.000 90.000 90.870 90.456 90.000 91.739 90.870 88.478 90.870 87.826 89.348 88.261 90.000 89.588 89.565 91.087 88.913 89.783 91.739 89.565 89.565 90.000 89.348 spambase 74.074 70.370 77.778 77.778 81.481 85.185 85.185 65.385 88.462 84.615 66.667 81.481 92.593 70.370 70.370 85.185 88.889 80.769 80.769 76.923 81.481 77.778 62.963 77.778 85.185 85.185 74.074 92.308 76.923 80.769 85.185 74.074 70.370 77.778 81.481 92.593 70.370 92.308 80.769 73.077 74.074 77.778 88.889 77.778 88.889 66.667 74.074 80.769 80.769 80.769 77.778 85.185 85.185 74.074 85.185 100.000 62.963 69.231 73.077 76.923 81.481 81.481 81.481 81.481 85.185 70.370 66.667 88.462 80.769 76.923 66.667 77.778 77.778 100.000 70.370 81.481 81.481 80.769 80.769 76.923 85.185 88.889 85.185 85.185 66.667 77.778 74.074 92.308 61.538 69.231 77.778 74.074 96.296 70.370 81.481 66.667 74.074 73.077 88.462 88.462 spect-reordered 95.611 94.984 96.238 94.671 96.238 95.611 95.611 93.103 94.984 96.552 94.984 96.552 94.984 93.730 94.984 97.179 94.357 96.865 95.298 94.984 94.357 95.298 95.925 96.865 96.552 95.611 95.298 96.238 94.671 93.417 93.417 96.865 96.238 95.611 94.984 94.357 96.552 96.238 94.671 95.611 96.238 95.611 94.984 96.552 96.865 95.298 96.552 92.790 94.357 94.044 95.298 94.357 94.044 94.357 95.611 95.298 97.492 95.298 96.238 96.552 96.552 94.357 95.925 95.611 94.357 94.357 95.298 93.417 95.611 98.433 94.044 96.238 94.984 95.611 93.730 94.357 93.730 97.806 96.552 96.552 95.925 93.417 96.552 95.611 95.925 95.611 94.671 95.298 96.238 95.925 96.865 93.417 97.179 95.611 97.179 95.298 95.611 93.103 95.298 95.298 splice 66.667 50.000 60.000 60.000 40.000 40.000 80.000 80.000 60.000 80.000 66.667 33.333 80.000 80.000 60.000 80.000 60.000 60.000 40.000 20.000 83.333 66.667 40.000 60.000 20.000 80.000 80.000 80.000 60.000 40.000 50.000 66.667 40.000 60.000 40.000 80.000 40.000 60.000 60.000 60.000 83.333 50.000 60.000 40.000 60.000 60.000 80.000 40.000 60.000 100.000 50.000 16.667 40.000 60.000 100.000 60.000 60.000 60.000 100.000 60.000 66.667 50.000 40.000 40.000 80.000 80.000 60.000 60.000 60.000 20.000 50.000 50.000 40.000 40.000 80.000 100.000 60.000 80.000 80.000 80.000 33.333 50.000 60.000 60.000 40.000 60.000 40.000 100.000 60.000 20.000 83.333 16.667 60.000 60.000 60.000 60.000 40.000 80.000 60.000 40.000 squash-stored 50.000 66.667 40.000 80.000 80.000 40.000 20.000 80.000 60.000 100.000 50.000 83.333 60.000 80.000 100.000 60.000 40.000 60.000 40.000 40.000 83.333 66.667 100.000 80.000 60.000 40.000 40.000 80.000 60.000 40.000 50.000 33.333 40.000 60.000 80.000 60.000 80.000 60.000 80.000 60.000 33.333 33.333 80.000 60.000 80.000 60.000 80.000 100.000 40.000 40.000 50.000 50.000 60.000 80.000 60.000 60.000 80.000 60.000 60.000 80.000 66.667 50.000 40.000 100.000 60.000 60.000 80.000 60.000 40.000 80.000 50.000 0.000 60.000 80.000 80.000 40.000 80.000 80.000 80.000 80.000 66.667 66.667 60.000 80.000 20.000 80.000 40.000 40.000 20.000 40.000 66.667 50.000 60.000 60.000 60.000 80.000 80.000 80.000 80.000 80.000 squash-unstored 50.000 40.000 60.000 46.667 60.000 40.000 40.000 40.000 46.667 46.667 37.500 46.667 60.000 53.333 33.333 40.000 40.000 33.333 60.000 46.667 43.750 40.000 53.333 33.333 53.333 46.667 53.333 46.667 46.667 53.333 37.500 46.667 46.667 40.000 33.333 60.000 40.000 53.333 53.333 60.000 43.750 53.333 46.667 40.000 46.667 46.667 53.333 46.667 53.333 40.000 31.250 53.333 60.000 40.000 33.333 46.667 46.667 53.333 46.667 46.667 31.250 46.667 46.667 40.000 53.333 53.333 53.333 60.000 46.667 40.000 50.000 26.667 46.667 40.000 53.333 33.333 40.000 53.333 40.000 60.000 50.000 46.667 60.000 46.667 46.667 46.667 40.000 40.000 53.333 40.000 43.750 46.667 46.667 46.667 40.000 33.333 46.667 53.333 46.667 53.333 tae 56.566 64.646 52.525 59.596 63.636 62.626 64.646 53.535 61.616 63.636 49.495 57.576 62.626 64.646 56.566 55.556 51.515 50.505 64.646 66.667 49.495 63.636 63.636 56.566 56.566 57.576 61.616 62.626 58.586 56.566 61.616 54.545 58.586 60.606 61.616 55.556 56.566 60.606 59.596 55.556 52.525 65.657 57.576 55.556 61.616 64.646 58.586 63.636 47.475 59.596 60.606 63.636 48.485 58.586 58.586 60.606 52.525 62.626 53.535 66.667 54.545 62.626 58.586 56.566 63.636 48.485 59.596 64.646 62.626 56.566 60.606 56.566 63.636 60.606 59.596 64.646 42.424 59.596 52.525 64.646 65.657 62.626 62.626 49.495 53.535 51.515 53.535 53.535 56.566 66.667 61.616 61.616 60.606 69.697 46.465 61.616 63.636 70.707 49.495 57.576 waveform 80.800 80.400 80.000 80.000 76.800 78.800 78.600 80.200 81.400 81.600 77.200 79.800 81.000 79.800 78.800 81.600 82.200 81.600 78.400 78.800 82.000 80.600 77.600 82.800 81.600 78.600 78.200 79.000 78.600 82.000 81.600 79.600 81.200 80.400 79.000 77.600 79.600 79.600 80.000 78.000 80.000 79.000 79.400 81.200 80.800 80.400 79.600 80.400 82.000 77.400 80.200 80.200 79.600 78.000 80.600 78.600 80.400 82.200 81.000 80.000 77.600 77.600 79.600 77.200 80.200 81.600 82.200 80.200 79.600 83.400 81.200 78.000 80.200 81.000 77.400 79.600 80.800 80.400 81.800 79.400 82.400 81.000 80.400 81.200 78.400 78.400 80.400 79.000 80.800 79.400 81.200 78.600 80.400 77.600 80.800 79.200 81.800 80.400 80.000 80.000 white-clover 85.714 42.857 57.143 83.333 66.667 100.000 50.000 83.333 50.000 50.000 57.143 42.857 57.143 66.667 83.333 83.333 83.333 66.667 66.667 100.000 42.857 57.143 42.857 83.333 66.667 66.667 66.667 66.667 83.333 50.000 85.714 85.714 71.429 66.667 33.333 66.667 50.000 66.667 50.000 66.667 85.714 100.000 57.143 50.000 50.000 50.000 66.667 83.333 66.667 50.000 42.857 42.857 57.143 50.000 83.333 33.333 100.000 83.333 83.333 33.333 57.143 57.143 71.429 83.333 50.000 50.000 33.333 66.667 66.667 33.333 71.429 100.000 57.143 83.333 66.667 66.667 66.667 50.000 66.667 66.667 71.429 71.429 71.429 66.667 66.667 66.667 50.000 50.000 66.667 66.667 85.714 71.429 42.857 66.667 83.333 83.333 50.000 83.333 83.333 16.667 wine 94.444 100.000 94.444 100.000 100.000 100.000 100.000 100.000 100.000 100.000 88.889 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 94.444 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 94.444 100.000 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 94.118 100.000 100.000 94.444 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 94.444 100.000 100.000 100.000 100.000 100.000 94.118 100.000 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 100.000 88.235 100.000 100.000 100.000 94.444 100.000 94.444 94.444 100.000 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 94.118 100.000 wisconsin-breast-cancer 100.000 95.714 97.143 94.286 97.143 98.571 97.143 98.571 97.143 95.652 97.143 98.571 95.714 97.143 100.000 94.286 98.571 94.286 97.143 98.551 100.000 95.714 95.714 98.571 94.286 97.143 98.571 97.143 95.714 97.101 98.571 95.714 97.143 98.571 95.714 98.571 98.571 98.571 95.714 97.101 98.571 95.714 95.714 94.286 97.143 97.143 100.000 97.143 98.571 98.551 98.571 98.571 97.143 97.143 95.714 97.143 100.000 94.286 95.714 98.551 94.286 97.143 97.143 94.286 98.571 98.571 98.571 97.143 94.286 100.000 97.143 100.000 98.571 95.714 95.714 95.714 95.714 95.714 100.000 98.551 98.571 98.571 97.143 94.286 97.143 97.143 98.571 97.143 98.571 94.203 95.714 95.714 100.000 98.571 92.857 97.143 98.571 100.000 97.143 97.101 yeast 57.718 55.034 59.060 58.389 56.081 60.811 61.486 58.108 48.649 52.703 59.732 61.745 57.047 54.362 65.541 50.676 49.324 56.757 56.757 65.541 58.389 63.087 55.705 56.376 64.865 58.784 52.027 52.027 58.784 58.784 58.389 57.718 52.349 59.732 52.703 60.135 60.811 55.405 58.784 58.784 57.047 61.074 63.087 55.705 56.757 55.405 57.432 59.459 55.405 53.378 50.336 57.718 58.389 60.403 55.405 64.865 59.459 62.838 54.730 57.432 59.060 57.718 57.047 54.362 62.838 64.189 55.405 56.757 58.108 50.676 54.362 59.060 62.416 53.691 62.162 56.757 53.378 59.459 53.378 62.162 52.349 57.718 61.745 60.403 56.081 62.162 53.378 56.757 64.865 53.378 59.060 57.718 52.349 60.403 57.432 58.784 58.784 58.784 55.405 55.405 zoo 81.818 100.000 100.000 90.000 80.000 100.000 90.000 100.000 90.000 100.000 90.909 90.000 90.000 100.000 90.000 90.000 100.000 100.000 100.000 90.000 90.909 90.000 100.000 90.000 90.000 100.000 90.000 90.000 100.000 100.000 90.909 100.000 100.000 90.000 100.000 80.000 100.000 90.000 90.000 100.000 100.000 100.000 80.000 100.000 90.000 100.000 70.000 90.000 100.000 100.000 90.909 100.000 90.000 100.000 100.000 80.000 90.000 100.000 100.000 100.000 100.000 90.000 100.000 100.000 90.000 100.000 100.000 90.000 100.000 90.000 100.000 100.000 90.000 90.000 100.000 80.000 100.000 80.000 100.000 100.000 90.909 100.000 100.000 90.000 100.000 90.000 80.000 90.000 100.000 100.000 81.818 100.000 90.000 80.000 100.000 100.000 100.000 80.000 90.000 100.000 aode anneal 96.667 100.000 96.667 100.000 97.778 96.667 100.000 96.667 96.629 98.876 97.778 98.889 98.889 96.667 98.889 96.667 96.667 95.556 94.382 100.000 100.000 98.889 97.778 98.889 95.556 98.889 97.778 97.778 95.506 98.876 97.778 98.889 97.778 98.889 94.444 96.667 98.889 98.889 100.000 96.629 96.667 98.889 95.556 97.778 98.889 98.889 98.889 96.667 100.000 98.876 96.667 98.889 100.000 96.667 96.667 97.778 97.778 95.556 100.000 97.753 97.778 98.889 97.778 100.000 94.444 96.667 98.889 97.778 97.753 98.876 100.000 97.778 96.667 97.778 97.778 98.889 96.667 98.889 95.506 100.000 96.667 93.333 96.667 100.000 98.889 100.000 95.556 96.667 98.876 98.876 97.778 100.000 97.778 98.889 97.778 97.778 98.889 98.889 96.629 97.753 audiology 73.913 69.565 78.261 60.870 69.565 82.609 72.727 77.273 72.727 59.091 60.870 78.261 73.913 73.913 60.870 60.870 77.273 77.273 72.727 72.727 73.913 60.870 69.565 78.261 73.913 69.565 81.818 68.182 77.273 68.182 52.174 73.913 69.565 65.217 73.913 73.913 86.364 68.182 72.727 81.818 69.565 69.565 78.261 69.565 78.261 73.913 72.727 68.182 72.727 63.636 82.609 73.913 73.913 60.870 65.217 73.913 77.273 72.727 68.182 68.182 65.217 69.565 78.261 65.217 69.565 78.261 77.273 68.182 81.818 63.636 65.217 69.565 82.609 69.565 73.913 65.217 72.727 81.818 63.636 72.727 65.217 73.913 69.565 73.913 73.913 73.913 77.273 81.818 68.182 68.182 78.261 69.565 73.913 73.913 56.522 73.913 77.273 72.727 72.727 59.091 cleeland-14 87.097 87.097 87.097 86.667 76.667 86.667 86.667 76.667 70.000 73.333 87.097 87.097 74.194 86.667 90.000 73.333 86.667 83.333 76.667 83.333 87.097 80.645 93.548 76.667 86.667 70.000 76.667 86.667 80.000 76.667 87.097 70.968 93.548 90.000 93.333 73.333 83.333 80.000 76.667 83.333 77.419 87.097 70.968 96.667 73.333 86.667 90.000 86.667 73.333 93.333 80.645 77.419 83.871 80.000 83.333 76.667 83.333 83.333 90.000 93.333 90.323 77.419 70.968 80.000 93.333 83.333 90.000 83.333 83.333 86.667 80.645 87.097 83.871 90.000 96.667 66.667 76.667 73.333 83.333 90.000 77.419 87.097 80.645 76.667 83.333 80.000 83.333 90.000 90.000 80.000 87.097 87.097 77.419 83.333 86.667 76.667 86.667 76.667 83.333 86.667 cmc 52.703 50.676 52.703 55.102 50.340 59.184 50.340 50.340 48.980 43.537 50.676 47.973 51.351 58.503 53.061 50.340 42.857 50.340 51.020 48.299 47.973 48.649 62.162 50.340 47.619 51.020 51.020 45.578 53.061 54.422 54.730 52.027 53.378 50.340 48.980 55.102 57.143 46.939 45.578 48.980 52.703 48.649 52.703 52.381 52.381 48.980 51.020 50.340 49.660 51.020 58.108 44.595 53.378 55.782 56.463 50.340 47.619 47.619 48.980 50.340 54.730 53.378 45.946 49.660 53.061 42.177 53.741 44.218 51.020 51.020 52.703 47.297 42.568 48.299 55.102 51.020 48.299 57.823 47.619 47.619 52.027 47.297 51.351 43.537 50.340 45.578 52.381 57.143 52.381 53.061 56.081 47.973 47.297 48.299 46.939 51.701 57.823 44.218 52.381 48.980 contact-lenses 66.667 66.667 66.667 66.667 50.000 100.000 100.000 100.000 0.000 100.000 66.667 66.667 66.667 100.000 100.000 100.000 0.000 100.000 100.000 100.000 66.667 66.667 66.667 100.000 50.000 50.000 100.000 50.000 100.000 50.000 33.333 66.667 66.667 100.000 100.000 100.000 100.000 50.000 100.000 0.000 66.667 66.667 66.667 33.333 100.000 100.000 100.000 100.000 50.000 50.000 66.667 100.000 33.333 33.333 50.000 100.000 100.000 100.000 100.000 100.000 66.667 66.667 100.000 100.000 100.000 100.000 100.000 50.000 50.000 50.000 100.000 100.000 100.000 33.333 100.000 100.000 0.000 50.000 50.000 50.000 66.667 100.000 66.667 66.667 100.000 50.000 100.000 0.000 100.000 100.000 66.667 0.000 100.000 33.333 100.000 50.000 100.000 100.000 100.000 100.000 credit 84.058 82.609 84.058 89.855 85.507 86.957 92.754 94.203 85.507 81.159 91.304 89.855 84.058 88.406 85.507 85.507 88.406 81.159 86.957 84.058 88.406 89.855 86.957 81.159 91.304 86.957 84.058 82.609 88.406 86.957 82.609 91.304 86.957 86.957 85.507 82.609 91.304 88.406 92.754 86.957 78.261 89.855 88.406 91.304 82.609 86.957 88.406 84.058 86.957 91.304 86.957 78.261 85.507 91.304 88.406 82.609 84.058 89.855 88.406 89.855 86.957 88.406 85.507 89.855 89.855 81.159 89.855 79.710 92.754 84.058 85.507 85.507 88.406 88.406 88.406 85.507 85.507 85.507 95.652 78.261 88.406 91.304 84.058 82.609 86.957 82.609 91.304 94.203 85.507 79.710 91.304 88.406 91.304 88.406 85.507 82.609 81.159 85.507 79.710 85.507 credit 84.058 82.609 84.058 89.855 85.507 86.957 92.754 94.203 85.507 81.159 91.304 89.855 84.058 88.406 85.507 85.507 88.406 81.159 86.957 84.058 88.406 89.855 86.957 81.159 91.304 86.957 84.058 82.609 88.406 86.957 82.609 91.304 86.957 86.957 85.507 82.609 91.304 88.406 92.754 86.957 78.261 89.855 88.406 91.304 82.609 86.957 88.406 84.058 86.957 91.304 86.957 78.261 85.507 91.304 88.406 82.609 84.058 89.855 88.406 89.855 86.957 88.406 85.507 89.855 89.855 81.159 89.855 79.710 92.754 84.058 85.507 85.507 88.406 88.406 88.406 85.507 85.507 85.507 95.652 78.261 88.406 91.304 84.058 82.609 86.957 82.609 91.304 94.203 85.507 79.710 91.304 88.406 91.304 88.406 85.507 82.609 81.159 85.507 79.710 85.507 ecoli 85.294 73.529 85.294 82.353 85.294 85.294 84.848 72.727 78.788 84.848 76.471 85.294 88.235 88.235 73.529 82.353 81.818 78.788 78.788 81.818 79.412 91.176 76.471 91.176 79.412 79.412 81.818 81.818 84.848 87.879 73.529 82.353 82.353 85.294 79.412 79.412 75.758 81.818 87.879 87.879 76.471 85.294 82.353 85.294 88.235 79.412 87.879 75.758 84.848 84.848 82.353 85.294 73.529 82.353 85.294 82.353 78.788 81.818 84.848 84.848 85.294 79.412 85.294 79.412 76.471 82.353 87.879 78.788 81.818 78.788 88.235 76.471 82.353 82.353 91.176 79.412 90.909 75.758 78.788 78.788 79.412 76.471 73.529 82.353 82.353 91.176 78.788 93.939 81.818 78.788 82.353 79.412 82.353 79.412 82.353 85.294 75.758 81.818 84.848 75.758 eucalyptus 54.054 66.216 58.108 45.946 60.811 58.108 72.603 54.795 61.644 54.795 56.757 58.108 68.919 52.703 67.568 66.216 49.315 60.274 67.123 64.384 56.757 52.703 62.162 62.162 67.568 56.757 54.795 60.274 67.123 69.863 59.459 62.162 63.514 47.297 55.405 68.919 69.863 65.753 58.904 58.904 66.216 62.162 58.108 64.865 62.162 64.865 56.164 52.055 56.164 60.274 62.162 63.514 62.162 59.459 62.162 58.108 61.644 72.603 56.164 54.795 59.459 58.108 59.459 54.054 59.459 56.757 58.904 49.315 61.644 71.233 51.351 56.757 64.865 59.459 66.216 59.459 54.795 53.425 64.384 61.644 59.459 56.757 68.919 70.270 54.054 59.459 52.055 53.425 65.753 50.685 63.514 58.108 60.811 59.459 60.811 63.514 57.534 64.384 60.274 53.425 german-credit 74.000 68.000 77.000 77.000 76.000 76.000 81.000 77.000 80.000 80.000 77.000 77.000 78.000 77.000 73.000 77.000 78.000 78.000 76.000 79.000 77.000 73.000 77.000 82.000 75.000 78.000 72.000 75.000 75.000 76.000 79.000 75.000 73.000 74.000 78.000 78.000 75.000 84.000 71.000 74.000 78.000 72.000 71.000 76.000 77.000 73.000 71.000 73.000 80.000 78.000 76.000 73.000 75.000 78.000 73.000 75.000 74.000 76.000 80.000 73.000 78.000 70.000 75.000 79.000 82.000 80.000 79.000 70.000 78.000 71.000 73.000 74.000 78.000 76.000 70.000 78.000 72.000 72.000 77.000 86.000 67.000 82.000 78.000 75.000 73.000 79.000 83.000 74.000 76.000 67.000 76.000 76.000 74.000 73.000 72.000 83.000 71.000 76.000 77.000 74.000 glass 63.636 81.818 72.727 77.273 76.190 85.714 61.905 80.952 71.429 66.667 68.182 81.818 72.727 63.636 76.190 76.190 80.952 71.429 71.429 80.952 81.818 81.818 81.818 68.182 71.429 71.429 71.429 90.476 66.667 76.190 59.091 72.727 77.273 72.727 71.429 80.952 52.381 80.952 80.952 80.952 63.636 68.182 77.273 77.273 76.190 76.190 66.667 85.714 80.952 76.190 81.818 81.818 77.273 68.182 85.714 76.190 61.905 66.667 85.714 66.667 68.182 68.182 77.273 77.273 80.952 90.476 85.714 66.667 76.190 76.190 72.727 77.273 90.909 72.727 71.429 66.667 85.714 66.667 66.667 80.952 72.727 68.182 86.364 77.273 71.429 76.190 66.667 76.190 66.667 80.952 81.818 81.818 72.727 59.091 90.476 71.429 71.429 52.381 71.429 66.667 grub-damage 43.750 56.250 37.500 50.000 25.000 60.000 26.667 53.333 40.000 46.667 43.750 50.000 37.500 37.500 37.500 60.000 46.667 53.333 40.000 33.333 37.500 43.750 56.250 50.000 43.750 66.667 46.667 46.667 40.000 53.333 43.750 62.500 43.750 43.750 43.750 40.000 46.667 46.667 53.333 53.333 56.250 37.500 50.000 56.250 43.750 40.000 20.000 66.667 46.667 53.333 31.250 43.750 43.750 56.250 43.750 33.333 40.000 60.000 46.667 53.333 43.750 37.500 37.500 50.000 37.500 40.000 46.667 40.000 46.667 66.667 25.000 56.250 37.500 56.250 50.000 46.667 53.333 33.333 53.333 53.333 50.000 56.250 56.250 43.750 31.250 66.667 40.000 26.667 53.333 26.667 31.250 62.500 50.000 37.500 56.250 33.333 40.000 53.333 33.333 53.333 haberman 74.194 74.194 77.419 74.194 74.194 70.968 70.000 70.000 70.000 70.000 70.968 77.419 67.742 74.194 74.194 70.968 73.333 70.000 76.667 73.333 70.968 80.645 67.742 74.194 64.516 58.065 73.333 80.000 66.667 73.333 64.516 77.419 77.419 74.194 74.194 70.968 76.667 80.000 76.667 66.667 74.194 67.742 67.742 70.968 80.645 74.194 76.667 70.000 73.333 66.667 74.194 74.194 74.194 77.419 77.419 70.968 73.333 76.667 70.000 66.667 67.742 70.968 77.419 67.742 80.645 70.968 70.000 70.000 60.000 83.333 61.290 70.968 77.419 67.742 70.968 70.968 76.667 76.667 66.667 73.333 77.419 70.968 83.871 70.968 67.742 77.419 66.667 63.333 66.667 83.333 70.968 70.968 74.194 74.194 77.419 74.194 73.333 86.667 76.667 66.667 hayes-roth 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 hepatitis 100.000 93.750 75.000 75.000 87.500 86.667 73.333 86.667 80.000 80.000 81.250 87.500 100.000 75.000 87.500 86.667 100.000 60.000 86.667 86.667 87.500 68.750 93.750 81.250 87.500 80.000 93.333 93.333 73.333 93.333 62.500 87.500 87.500 81.250 68.750 93.333 86.667 86.667 93.333 86.667 93.750 87.500 81.250 100.000 75.000 93.333 86.667 80.000 66.667 86.667 87.500 93.750 75.000 81.250 87.500 80.000 93.333 86.667 80.000 73.333 87.500 93.750 81.250 87.500 87.500 73.333 66.667 86.667 93.333 93.333 87.500 100.000 62.500 75.000 87.500 73.333 86.667 86.667 100.000 86.667 68.750 93.750 75.000 93.750 87.500 86.667 73.333 80.000 80.000 93.333 93.750 87.500 75.000 100.000 75.000 80.000 66.667 93.333 73.333 93.333 hungarian-14 73.333 76.667 90.000 86.667 89.655 89.655 79.310 93.103 82.759 82.759 80.000 86.667 90.000 86.667 89.655 79.310 72.414 86.207 86.207 86.207 80.000 83.333 86.667 83.333 96.552 82.759 86.207 93.103 75.862 75.862 90.000 76.667 83.333 83.333 68.966 93.103 75.862 89.655 93.103 93.103 90.000 83.333 76.667 80.000 93.103 93.103 72.414 79.310 86.207 93.103 83.333 83.333 86.667 86.667 82.759 93.103 75.862 79.310 86.207 86.207 90.000 80.000 86.667 86.667 82.759 86.207 79.310 93.103 75.862 82.759 80.000 73.333 93.333 86.667 82.759 86.207 86.207 82.759 82.759 86.207 83.333 80.000 76.667 90.000 86.207 82.759 82.759 79.310 93.103 93.103 73.333 83.333 86.667 96.667 86.207 86.207 82.759 82.759 75.862 89.655 hypothyroid 98.148 98.677 98.408 98.939 98.143 98.408 99.735 98.674 98.674 97.613 98.413 99.471 98.408 98.143 98.674 98.143 98.408 98.143 98.674 98.674 98.677 99.206 98.674 98.674 98.408 97.613 97.878 97.347 98.408 98.939 98.148 97.884 98.674 99.469 98.408 98.408 98.939 98.143 99.204 98.408 98.677 98.148 97.613 99.204 97.613 98.143 99.469 98.939 99.469 98.674 97.090 98.677 97.878 99.469 99.204 97.878 98.143 98.143 98.674 98.408 99.206 98.677 97.613 98.939 98.674 98.674 98.939 98.674 97.347 98.939 97.884 97.619 99.735 98.939 97.878 99.204 97.878 98.939 98.674 98.939 98.677 97.354 99.735 97.347 99.204 98.674 98.143 98.674 98.143 99.204 99.735 98.677 99.204 97.878 99.204 97.878 98.939 98.143 98.143 98.408 ionosphere 91.667 85.714 88.571 91.429 94.286 94.286 94.286 88.571 91.429 88.571 88.889 82.857 94.286 85.714 97.143 91.429 94.286 97.143 97.143 82.857 88.889 97.143 91.429 77.143 97.143 80.000 97.143 91.429 88.571 94.286 91.667 85.714 97.143 94.286 88.571 91.429 91.429 91.429 94.286 88.571 91.667 97.143 88.571 97.143 88.571 94.286 91.429 82.857 85.714 97.143 86.111 85.714 94.286 91.429 91.429 100.000 91.429 94.286 94.286 85.714 88.889 80.000 85.714 91.429 94.286 97.143 80.000 88.571 97.143 97.143 86.111 94.286 85.714 91.429 88.571 91.429 88.571 97.143 94.286 94.286 88.889 91.429 91.429 94.286 82.857 97.143 85.714 91.429 94.286 94.286 97.222 97.143 91.429 88.571 94.286 85.714 88.571 94.286 85.714 94.286 iris 93.333 100.000 100.000 100.000 93.333 93.333 86.667 86.667 80.000 93.333 93.333 80.000 86.667 100.000 100.000 93.333 100.000 93.333 86.667 100.000 100.000 86.667 93.333 93.333 86.667 100.000 86.667 86.667 93.333 93.333 86.667 93.333 80.000 93.333 100.000 93.333 100.000 100.000 93.333 100.000 100.000 73.333 93.333 100.000 93.333 100.000 93.333 86.667 93.333 93.333 93.333 100.000 93.333 86.667 93.333 93.333 93.333 93.333 100.000 100.000 93.333 86.667 86.667 93.333 93.333 100.000 93.333 93.333 93.333 93.333 86.667 93.333 93.333 100.000 100.000 80.000 93.333 100.000 86.667 86.667 93.333 100.000 86.667 93.333 93.333 100.000 93.333 93.333 93.333 93.333 100.000 93.333 100.000 86.667 93.333 93.333 93.333 80.000 93.333 93.333 kr-s-kp 91.250 90.312 91.875 90.625 90.625 93.750 90.909 90.596 91.536 90.909 92.812 90.938 91.250 89.375 93.125 89.688 89.655 89.655 89.655 93.103 87.812 91.250 89.375 89.375 91.562 92.812 93.417 89.342 92.163 93.417 93.750 91.562 90.625 89.688 90.000 92.812 89.342 91.850 87.461 91.536 90.938 91.250 89.375 91.250 90.938 90.000 91.223 94.357 91.223 89.655 89.688 87.812 94.375 91.875 89.375 91.562 90.909 92.790 92.476 88.088 91.562 89.062 94.375 91.562 90.938 89.062 90.596 91.223 93.730 89.655 92.188 90.625 91.875 89.062 93.125 89.688 93.417 91.850 89.969 87.774 90.312 92.188 93.750 86.250 90.312 93.125 90.282 91.223 93.103 89.655 91.250 91.875 94.062 91.250 90.000 89.688 90.282 90.282 91.536 91.536 labor 66.667 66.667 83.333 66.667 100.000 100.000 83.333 80.000 100.000 100.000 83.333 83.333 100.000 83.333 83.333 100.000 100.000 100.000 80.000 60.000 100.000 83.333 83.333 100.000 100.000 83.333 66.667 100.000 100.000 80.000 100.000 83.333 100.000 83.333 83.333 100.000 66.667 100.000 80.000 80.000 66.667 100.000 83.333 100.000 100.000 66.667 83.333 100.000 100.000 100.000 83.333 83.333 83.333 100.000 83.333 83.333 83.333 100.000 80.000 60.000 83.333 100.000 83.333 83.333 100.000 100.000 83.333 100.000 100.000 80.000 83.333 100.000 100.000 83.333 83.333 100.000 83.333 100.000 80.000 100.000 83.333 83.333 100.000 100.000 100.000 83.333 100.000 60.000 100.000 80.000 100.000 100.000 100.000 66.667 66.667 100.000 100.000 60.000 100.000 100.000 lier-disorders 48.571 57.143 54.286 57.143 57.143 58.824 58.824 55.882 52.941 61.765 57.143 57.143 57.143 57.143 57.143 55.882 55.882 58.824 50.000 58.824 51.429 51.429 62.857 54.286 57.143 58.824 58.824 58.824 55.882 58.824 54.286 62.857 57.143 57.143 62.857 58.824 55.882 58.824 58.824 52.941 51.429 57.143 57.143 60.000 57.143 55.882 58.824 61.765 58.824 50.000 51.429 60.000 42.857 57.143 57.143 58.824 58.824 58.824 50.000 58.824 60.000 57.143 54.286 57.143 57.143 38.235 58.824 55.882 58.824 58.824 54.286 60.000 57.143 57.143 57.143 58.824 61.765 61.765 52.941 58.824 45.714 57.143 51.429 60.000 57.143 58.824 58.824 58.824 61.765 58.824 62.857 62.857 57.143 62.857 48.571 58.824 58.824 50.000 61.765 58.824 lymphography 93.333 93.333 93.333 80.000 86.667 86.667 86.667 80.000 78.571 78.571 93.333 93.333 86.667 80.000 86.667 93.333 80.000 73.333 92.857 85.714 86.667 93.333 93.333 86.667 80.000 80.000 80.000 86.667 85.714 100.000 80.000 73.333 93.333 86.667 86.667 93.333 73.333 93.333 92.857 100.000 80.000 100.000 86.667 100.000 86.667 80.000 86.667 86.667 71.429 100.000 93.333 86.667 86.667 73.333 100.000 93.333 66.667 93.333 92.857 92.857 66.667 80.000 86.667 80.000 93.333 93.333 86.667 86.667 85.714 92.857 80.000 86.667 80.000 86.667 100.000 80.000 93.333 86.667 100.000 85.714 93.333 86.667 86.667 93.333 80.000 86.667 80.000 80.000 78.571 92.857 86.667 80.000 86.667 80.000 86.667 93.333 80.000 93.333 100.000 85.714 monks 60.656 63.333 63.333 61.667 63.333 63.333 68.333 66.667 63.333 63.333 62.295 65.000 65.000 68.333 61.667 61.667 65.000 66.667 60.000 65.000 62.295 65.000 66.667 65.000 58.333 65.000 65.000 66.667 66.667 65.000 65.574 63.333 65.000 63.333 66.667 61.667 65.000 65.000 63.333 66.667 63.934 63.333 65.000 60.000 65.000 65.000 65.000 68.333 66.667 68.333 67.213 66.667 66.667 65.000 66.667 63.333 63.333 70.000 63.333 65.000 62.295 63.333 61.667 61.667 63.333 65.000 65.000 66.667 60.000 66.667 62.295 65.000 63.333 66.667 68.333 65.000 63.333 61.667 65.000 63.333 67.213 63.333 65.000 63.333 65.000 66.667 65.000 65.000 65.000 66.667 59.016 63.333 68.333 65.000 66.667 65.000 65.000 61.667 66.667 63.333 monks1 80.357 83.929 91.071 83.929 78.571 92.857 78.182 90.909 85.455 89.091 94.643 85.714 75.000 83.929 91.071 82.143 83.636 80.000 83.636 80.000 83.929 80.357 85.714 82.143 78.571 87.500 87.273 89.091 85.455 83.636 94.643 85.714 80.357 82.143 80.357 83.929 83.636 89.091 78.182 81.818 80.357 80.357 92.857 87.500 89.286 83.929 87.273 78.182 83.636 76.364 87.500 82.143 89.286 82.143 82.143 85.714 81.818 85.455 85.455 92.727 87.500 80.357 85.714 80.357 91.071 89.286 78.182 87.273 87.273 81.818 82.143 91.071 83.929 80.357 85.714 83.929 83.636 80.000 87.273 89.091 76.786 82.143 92.857 80.357 87.500 83.929 85.455 83.636 83.636 89.091 89.286 83.929 85.714 78.571 83.929 92.857 80.000 90.909 85.455 80.000 monks3 96.429 98.214 92.857 98.214 100.000 98.182 94.545 94.545 100.000 94.545 94.643 94.643 100.000 96.429 96.364 98.182 100.000 96.364 94.545 98.182 100.000 92.857 98.214 89.286 98.182 100.000 96.364 94.545 96.364 100.000 100.000 92.857 98.214 98.214 96.364 92.727 100.000 98.182 94.545 94.545 96.429 100.000 96.429 98.214 92.727 96.364 96.364 94.545 98.182 98.182 100.000 94.643 92.857 100.000 98.182 98.182 98.182 94.545 94.545 96.364 98.214 100.000 98.214 96.429 90.909 98.182 94.545 98.182 98.182 96.364 91.071 98.214 100.000 98.214 94.545 96.364 98.182 96.364 92.727 100.000 94.643 98.214 96.429 96.429 96.364 96.364 94.545 100.000 98.182 96.364 98.214 100.000 96.429 92.857 94.545 100.000 98.182 96.364 98.182 92.727 mushroom 99.877 99.877 99.877 100.000 99.877 100.000 100.000 100.000 100.000 100.000 99.877 100.000 99.877 100.000 100.000 100.000 99.877 99.877 100.000 100.000 100.000 100.000 99.877 100.000 100.000 99.877 100.000 99.877 99.877 100.000 100.000 100.000 100.000 99.877 99.877 100.000 99.754 100.000 100.000 100.000 100.000 100.000 99.754 100.000 100.000 99.877 100.000 99.877 100.000 100.000 99.877 100.000 100.000 99.877 100.000 100.000 99.877 100.000 99.877 100.000 99.877 100.000 100.000 100.000 99.877 99.877 100.000 100.000 99.877 100.000 100.000 100.000 100.000 99.877 100.000 99.877 100.000 99.754 100.000 100.000 99.877 100.000 100.000 100.000 100.000 100.000 99.877 100.000 99.877 99.877 99.877 99.877 100.000 99.877 100.000 99.877 100.000 100.000 100.000 100.000 nursery 92.747 91.898 93.750 92.130 93.056 93.056 92.052 93.056 91.127 94.213 93.827 91.898 92.438 92.978 93.364 92.593 92.593 92.593 92.670 92.361 92.361 92.515 92.593 93.596 92.670 93.364 92.747 92.747 92.130 93.441 93.210 93.827 92.284 92.978 93.056 92.052 92.901 92.207 92.515 92.515 92.130 92.438 92.824 93.673 91.975 93.287 93.287 92.361 92.361 91.898 92.515 91.590 92.284 93.210 92.670 93.596 93.133 92.515 93.441 91.435 93.673 93.287 93.210 91.975 93.596 92.747 91.744 92.052 92.824 92.747 93.210 92.438 91.127 92.670 92.670 93.210 93.056 92.901 92.978 92.824 92.361 92.515 92.901 93.287 93.441 93.519 93.904 92.438 91.667 92.593 93.056 93.519 92.361 92.747 92.207 91.821 92.515 92.824 93.596 92.361 optdigits 96.619 97.509 96.797 96.797 96.263 96.263 98.399 97.509 96.975 95.907 97.509 97.687 97.331 95.730 96.975 97.687 97.331 96.085 95.730 96.975 95.907 97.687 96.441 96.975 98.221 96.263 95.374 97.153 98.577 96.797 96.263 97.509 95.374 96.619 98.043 98.043 96.263 97.153 96.975 96.975 95.552 98.043 96.975 96.975 96.441 96.441 97.331 97.331 96.797 97.153 95.907 96.975 96.441 97.153 97.153 96.975 97.687 96.975 97.509 97.687 95.907 98.399 95.552 96.797 96.975 97.153 96.975 98.043 96.619 96.975 96.263 96.263 96.619 97.331 96.619 97.153 97.331 97.331 96.085 98.399 97.153 96.263 96.263 95.374 97.153 96.797 96.263 96.975 98.399 98.577 95.552 95.907 96.085 95.730 98.577 96.619 97.331 97.509 97.687 96.441 owel 97.263 95.803 96.350 96.892 96.709 96.892 97.258 97.806 96.892 97.623 97.263 96.533 96.533 96.527 97.623 97.258 97.441 96.709 97.441 95.978 97.445 97.080 96.898 95.795 97.806 96.892 96.161 97.258 97.441 97.441 97.445 97.080 96.715 95.612 97.075 97.989 97.441 96.892 97.075 94.150 97.263 97.445 96.350 97.623 95.795 97.075 97.989 96.892 96.161 96.709 98.358 96.715 95.985 97.075 95.247 94.881 97.441 97.441 97.806 97.989 96.350 98.358 97.263 97.623 95.978 96.161 97.258 95.612 97.623 97.258 97.080 97.993 96.715 97.258 97.441 96.892 97.989 96.344 95.978 96.892 96.715 96.715 98.175 96.344 95.795 95.795 96.892 97.258 97.258 97.623 95.803 97.263 97.263 97.075 97.258 97.258 96.161 97.075 97.623 96.161 page-blocks 75.000 75.000 75.000 100.000 100.000 50.000 66.667 66.667 100.000 100.000 75.000 100.000 50.000 75.000 100.000 100.000 100.000 66.667 100.000 66.667 50.000 75.000 100.000 100.000 75.000 75.000 33.333 100.000 100.000 100.000 75.000 100.000 50.000 100.000 75.000 75.000 66.667 100.000 100.000 66.667 100.000 75.000 50.000 75.000 50.000 75.000 100.000 100.000 66.667 100.000 75.000 100.000 75.000 50.000 75.000 100.000 100.000 100.000 66.667 100.000 100.000 75.000 75.000 75.000 100.000 50.000 66.667 33.333 66.667 100.000 50.000 50.000 100.000 75.000 100.000 75.000 66.667 100.000 66.667 100.000 50.000 75.000 75.000 100.000 100.000 75.000 100.000 100.000 66.667 66.667 75.000 50.000 75.000 100.000 50.000 75.000 100.000 33.333 100.000 100.000 pasture-production 96.545 97.909 98.726 97.452 98.362 97.361 98.089 98.089 97.725 97.907 98.091 98.273 97.634 97.452 97.998 97.998 97.452 97.543 97.634 96.815 97.909 98.091 97.088 97.270 97.634 97.998 97.998 97.634 98.544 98.180 97.818 97.636 98.180 97.452 97.452 97.361 97.998 97.725 98.726 97.270 97.909 98.364 96.997 97.361 97.634 97.361 98.089 98.089 97.634 97.907 97.545 97.545 97.725 97.543 98.453 97.179 98.271 97.725 97.270 98.817 97.545 97.818 97.998 97.270 97.634 97.725 98.362 97.452 98.180 97.998 97.364 98.273 97.816 97.816 97.361 97.907 97.270 98.180 97.543 97.179 97.636 98.182 97.907 97.816 97.452 97.634 97.725 98.180 97.088 98.089 97.727 98.182 97.361 97.725 97.725 97.907 97.452 97.816 97.543 97.998 pendigits 72.727 71.429 81.818 80.519 74.026 74.026 66.234 72.727 73.684 82.895 74.026 74.026 76.623 71.429 76.623 80.519 80.519 74.026 81.579 69.737 72.727 71.429 76.623 76.623 83.117 70.130 74.026 74.026 82.895 81.579 80.519 77.922 76.623 71.429 74.026 77.922 77.922 83.117 75.000 72.368 80.519 71.429 79.221 77.922 76.623 75.325 74.026 76.623 69.737 71.053 71.429 77.922 68.831 80.519 72.727 77.922 68.831 85.714 82.895 67.105 68.831 74.026 79.221 66.234 76.623 67.532 84.416 77.922 78.947 78.947 85.714 77.922 72.727 76.623 75.325 76.623 71.429 68.831 68.421 81.579 72.727 79.221 70.130 79.221 67.532 75.325 75.325 83.117 77.632 77.632 77.922 76.623 77.922 72.727 76.623 76.623 81.818 75.325 78.947 64.474 pima-diabetes 66.667 66.667 77.778 66.667 66.667 77.778 66.667 66.667 66.667 55.556 66.667 66.667 55.556 77.778 66.667 55.556 66.667 66.667 66.667 66.667 77.778 77.778 77.778 66.667 55.556 66.667 55.556 66.667 55.556 66.667 66.667 77.778 66.667 77.778 66.667 66.667 55.556 66.667 66.667 66.667 66.667 66.667 66.667 44.444 77.778 77.778 66.667 66.667 55.556 66.667 77.778 66.667 77.778 77.778 66.667 66.667 55.556 66.667 66.667 66.667 77.778 77.778 77.778 66.667 55.556 66.667 66.667 66.667 66.667 55.556 55.556 66.667 77.778 55.556 55.556 66.667 66.667 66.667 66.667 66.667 66.667 66.667 55.556 66.667 55.556 44.444 77.778 77.778 77.778 77.778 55.556 66.667 66.667 66.667 77.778 66.667 55.556 77.778 66.667 66.667 postoperatie 47.059 52.941 50.000 44.118 47.059 52.941 44.118 44.118 47.059 45.455 35.294 41.176 41.176 41.176 52.941 52.941 55.882 44.118 50.000 54.545 55.882 55.882 41.176 55.882 50.000 50.000 52.941 44.118 38.235 45.455 47.059 52.941 41.176 47.059 61.765 47.059 47.059 44.118 47.059 39.394 47.059 52.941 52.941 61.765 44.118 52.941 47.059 47.059 35.294 45.455 44.118 38.235 35.294 55.882 52.941 47.059 38.235 52.941 47.059 57.576 38.235 44.118 50.000 58.824 50.000 50.000 55.882 47.059 44.118 51.515 50.000 47.059 58.824 52.941 44.118 50.000 41.176 41.176 50.000 36.364 44.118 47.059 52.941 52.941 55.882 50.000 35.294 52.941 38.235 48.485 50.000 52.941 61.765 44.118 47.059 58.824 38.235 44.118 47.059 39.394 primary-tumor 93.074 95.671 95.671 94.372 95.671 93.074 92.208 96.970 94.372 93.939 94.805 95.238 95.671 93.939 94.805 96.104 94.372 93.939 96.104 95.671 97.403 97.403 93.939 94.372 93.506 93.939 93.506 93.939 95.671 93.939 96.104 94.372 93.506 95.671 95.671 94.372 95.238 93.074 95.238 96.104 94.805 94.372 93.939 95.671 96.537 96.104 94.805 95.238 94.372 95.238 96.970 92.641 95.238 96.104 96.537 95.238 94.805 94.372 93.506 94.372 95.671 96.104 96.537 97.403 93.939 96.104 93.939 94.805 95.671 93.506 95.671 96.970 92.641 94.372 98.268 93.939 94.805 96.537 94.805 96.537 96.537 95.671 92.641 96.537 94.372 96.970 96.537 95.671 95.238 96.104 95.671 93.506 96.104 91.342 96.104 96.104 92.208 95.238 95.671 96.970 segment 90.909 87.879 87.879 90.625 87.500 93.750 87.500 84.375 84.375 90.625 87.879 87.879 84.848 90.625 93.750 93.750 87.500 87.500 81.250 87.500 87.879 87.879 87.879 81.250 90.625 96.875 93.750 87.500 87.500 84.375 90.909 87.879 81.818 96.875 93.750 87.500 84.375 87.500 87.500 87.500 87.879 87.879 84.848 90.625 87.500 87.500 87.500 87.500 93.750 87.500 87.879 87.879 90.909 87.500 87.500 87.500 93.750 87.500 84.375 87.500 81.818 87.879 90.909 87.500 90.625 87.500 87.500 93.750 87.500 90.625 81.818 87.879 84.848 93.750 90.625 93.750 87.500 81.250 87.500 87.500 90.909 87.879 84.848 90.625 87.500 87.500 90.625 87.500 87.500 90.625 87.879 84.848 87.879 90.625 87.500 90.625 87.500 87.500 90.625 90.625 solar-flare-C 93.939 84.848 87.879 84.375 87.500 90.625 87.500 84.375 90.625 87.500 84.848 84.848 87.879 87.500 87.500 84.375 84.375 87.500 81.250 90.625 90.909 84.848 84.848 87.500 93.750 93.750 87.500 84.375 81.250 90.625 84.848 87.879 87.879 84.375 87.500 87.500 84.375 90.625 90.625 81.250 90.909 87.879 84.848 87.500 87.500 87.500 81.250 87.500 90.625 87.500 87.879 87.879 84.848 87.500 87.500 87.500 90.625 87.500 78.125 90.625 84.848 78.788 87.879 90.625 87.500 90.625 90.625 90.625 81.250 90.625 90.909 81.818 81.818 90.625 87.500 87.500 87.500 87.500 90.625 90.625 84.848 87.879 87.879 90.625 87.500 84.375 90.625 84.375 87.500 90.625 90.909 90.909 87.879 87.500 87.500 87.500 87.500 87.500 78.125 90.625 solar-flare-X 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 93.939 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 solar-flare-m 85.714 76.190 80.952 85.714 76.190 71.429 85.714 85.714 80.000 85.000 76.190 71.429 71.429 76.190 66.667 57.143 80.952 80.952 95.000 95.000 66.667 85.714 90.476 90.476 80.952 66.667 57.143 66.667 60.000 80.000 66.667 90.476 76.190 66.667 80.952 71.429 66.667 66.667 95.000 85.000 71.429 76.190 76.190 85.714 66.667 80.952 71.429 90.476 95.000 85.000 90.476 90.476 76.190 66.667 71.429 61.905 85.714 80.952 75.000 60.000 85.714 80.952 66.667 85.714 76.190 85.714 57.143 71.429 85.000 85.000 71.429 85.714 71.429 76.190 71.429 66.667 80.952 85.714 85.000 80.000 76.190 76.190 90.476 80.952 61.905 80.952 71.429 71.429 70.000 80.000 80.952 71.429 85.714 61.905 66.667 71.429 80.952 90.476 60.000 70.000 sonar 95.652 94.203 95.652 91.176 94.118 91.176 98.529 92.647 91.176 89.706 92.754 94.203 91.304 98.529 94.118 97.059 92.647 91.176 86.765 92.647 100.000 94.203 94.203 92.647 91.176 91.176 91.176 97.059 94.118 89.706 94.203 95.652 92.754 94.118 88.235 94.118 94.118 95.588 94.118 91.176 92.754 89.855 94.203 94.118 95.588 88.235 92.647 97.059 92.647 95.588 85.507 95.652 95.652 95.588 91.176 95.588 92.647 91.176 94.118 92.647 91.304 94.203 92.754 94.118 92.647 94.118 91.176 98.529 89.706 95.588 91.304 94.203 97.101 97.059 86.765 98.529 88.235 86.765 94.118 97.059 91.304 88.406 92.754 95.588 98.529 94.118 94.118 92.647 92.647 95.588 92.754 95.652 94.203 95.588 92.647 94.118 92.647 94.118 88.235 92.647 soybean 94.794 93.913 91.304 93.043 93.261 92.174 92.826 94.783 94.565 93.043 93.492 91.522 93.478 93.261 92.391 92.826 95.217 92.609 93.478 92.391 93.059 94.348 93.478 93.043 92.391 92.826 94.130 90.217 93.261 95.217 90.672 93.043 94.130 91.739 93.261 96.087 94.130 92.609 92.609 91.304 91.757 91.739 93.696 92.174 93.913 93.913 96.087 93.913 92.174 92.826 91.106 93.043 92.391 91.957 93.913 93.478 92.609 93.913 92.174 93.696 91.974 93.261 93.913 94.130 92.826 91.087 92.609 92.609 95.870 92.609 91.323 91.957 93.478 93.261 96.522 93.043 92.174 92.391 93.696 93.478 91.974 93.261 94.783 94.783 93.261 93.043 92.609 93.261 91.304 92.609 93.709 93.043 91.739 93.478 93.696 93.913 93.696 92.391 93.261 92.609 spambase 74.074 66.667 81.481 81.481 85.185 81.481 88.889 73.077 88.462 88.462 74.074 81.481 88.889 70.370 77.778 92.593 85.185 84.615 88.462 76.923 85.185 81.481 66.667 74.074 88.889 81.481 77.778 92.308 73.077 84.615 85.185 74.074 74.074 77.778 81.481 92.593 70.370 92.308 80.769 73.077 74.074 81.481 85.185 74.074 92.593 77.778 74.074 80.769 80.769 84.615 85.185 85.185 88.889 74.074 81.481 100.000 70.370 69.231 76.923 80.769 81.481 81.481 81.481 81.481 96.296 77.778 70.370 84.615 76.923 80.769 66.667 85.185 74.074 100.000 70.370 85.185 85.185 84.615 80.769 76.923 81.481 96.296 88.889 88.889 66.667 81.481 77.778 88.462 61.538 76.923 77.778 70.370 96.296 77.778 77.778 66.667 81.481 80.769 88.462 88.462 spect-reordered 95.611 96.552 96.865 96.552 97.806 96.238 95.925 93.730 95.925 96.865 94.357 97.179 95.925 94.357 95.298 97.492 95.611 96.552 97.179 96.865 95.925 95.925 95.611 97.179 96.865 95.925 95.925 96.552 96.238 94.671 94.044 96.552 98.119 96.238 94.984 95.298 96.552 95.611 95.925 96.238 96.552 96.865 96.552 97.179 96.865 95.925 97.179 94.044 94.044 96.238 95.925 95.611 94.671 95.298 96.238 95.925 97.492 96.865 96.552 97.179 95.925 96.238 96.865 95.611 96.238 94.984 95.925 94.044 96.865 98.119 95.925 97.806 96.238 95.298 95.611 94.984 94.984 97.806 96.865 96.865 97.179 94.357 96.865 97.179 96.865 95.925 95.298 96.552 96.865 95.298 97.492 94.357 97.179 95.925 97.492 95.611 95.611 94.984 95.925 94.984 splice 66.667 33.333 60.000 60.000 60.000 60.000 60.000 60.000 80.000 60.000 66.667 33.333 100.000 80.000 60.000 80.000 60.000 40.000 80.000 20.000 83.333 66.667 40.000 40.000 20.000 60.000 80.000 80.000 40.000 40.000 83.333 50.000 40.000 60.000 40.000 60.000 40.000 40.000 60.000 60.000 100.000 50.000 60.000 80.000 60.000 60.000 80.000 40.000 60.000 80.000 50.000 16.667 40.000 60.000 100.000 60.000 40.000 60.000 80.000 80.000 66.667 66.667 40.000 40.000 80.000 60.000 80.000 60.000 60.000 40.000 50.000 50.000 40.000 40.000 80.000 80.000 60.000 80.000 80.000 80.000 33.333 50.000 60.000 80.000 40.000 80.000 40.000 80.000 60.000 20.000 50.000 33.333 60.000 60.000 60.000 40.000 80.000 100.000 80.000 40.000 squash-stored 66.667 100.000 40.000 80.000 60.000 60.000 60.000 80.000 60.000 100.000 50.000 83.333 80.000 80.000 100.000 80.000 80.000 60.000 20.000 40.000 83.333 66.667 100.000 80.000 60.000 40.000 60.000 60.000 80.000 60.000 50.000 33.333 40.000 60.000 80.000 60.000 100.000 60.000 60.000 80.000 33.333 50.000 100.000 60.000 100.000 60.000 80.000 100.000 60.000 40.000 83.333 50.000 80.000 60.000 80.000 60.000 80.000 60.000 60.000 80.000 66.667 50.000 80.000 100.000 60.000 100.000 100.000 40.000 40.000 80.000 50.000 0.000 60.000 80.000 60.000 40.000 80.000 100.000 80.000 60.000 66.667 66.667 60.000 80.000 20.000 80.000 100.000 60.000 80.000 60.000 66.667 50.000 40.000 80.000 60.000 80.000 80.000 80.000 80.000 80.000 squash-unstored 50.000 40.000 60.000 46.667 60.000 40.000 40.000 40.000 46.667 46.667 37.500 46.667 60.000 53.333 53.333 40.000 40.000 33.333 60.000 46.667 43.750 40.000 53.333 33.333 53.333 46.667 53.333 46.667 46.667 53.333 37.500 46.667 46.667 40.000 33.333 60.000 40.000 53.333 53.333 60.000 43.750 53.333 46.667 40.000 46.667 46.667 53.333 46.667 53.333 40.000 31.250 53.333 60.000 40.000 33.333 46.667 46.667 53.333 46.667 46.667 31.250 46.667 46.667 40.000 53.333 53.333 53.333 60.000 46.667 40.000 50.000 26.667 46.667 40.000 53.333 53.333 40.000 53.333 40.000 60.000 50.000 46.667 60.000 46.667 46.667 46.667 40.000 40.000 53.333 40.000 43.750 46.667 46.667 46.667 40.000 33.333 46.667 53.333 46.667 53.333 tae 76.768 83.838 76.768 76.768 81.818 74.747 78.788 71.717 78.788 77.778 74.747 78.788 82.828 81.818 76.768 64.646 72.727 78.788 70.707 80.808 68.687 74.747 81.818 73.737 67.677 77.778 77.778 78.788 74.747 68.687 75.758 74.747 79.798 81.818 77.778 66.667 68.687 80.808 79.798 75.758 71.717 80.808 71.717 80.808 74.747 76.768 68.687 77.778 67.677 84.848 78.788 80.808 65.657 79.798 80.808 75.758 74.747 75.758 75.758 80.808 79.798 81.818 74.747 74.747 69.697 69.697 69.697 77.778 81.818 74.747 77.778 74.747 82.828 71.717 73.737 76.768 60.606 73.737 67.677 79.798 77.778 74.747 75.758 72.727 69.697 72.727 71.717 75.758 80.808 78.788 74.747 79.798 71.717 77.778 69.697 75.758 74.747 84.848 70.707 74.747 waveform 87.400 84.600 84.400 85.400 82.000 86.200 84.200 83.800 83.800 87.400 83.400 86.400 86.400 80.800 86.200 88.400 86.000 86.200 85.200 82.800 85.800 86.400 83.400 87.200 88.000 84.000 83.600 84.600 84.000 84.000 83.800 82.400 86.200 85.000 83.600 83.200 86.200 85.200 86.800 85.400 84.400 85.000 85.000 86.200 87.400 83.600 85.800 83.400 85.400 83.000 85.200 85.600 83.600 84.800 86.800 83.800 84.400 85.600 84.000 86.400 83.800 84.400 84.000 84.400 85.800 84.800 87.400 84.400 84.200 88.200 89.000 85.200 83.800 84.600 85.000 84.200 85.400 84.400 85.800 86.200 85.600 84.400 86.800 87.200 83.800 84.600 86.000 82.400 82.600 84.600 86.200 82.200 84.200 84.000 85.600 84.000 84.400 85.000 86.600 86.400 white-clover 85.714 42.857 57.143 83.333 83.333 100.000 50.000 83.333 50.000 50.000 57.143 42.857 71.429 66.667 100.000 83.333 83.333 66.667 66.667 100.000 42.857 57.143 42.857 83.333 66.667 66.667 66.667 66.667 83.333 50.000 85.714 85.714 71.429 66.667 50.000 83.333 50.000 66.667 50.000 66.667 85.714 100.000 57.143 50.000 50.000 50.000 66.667 83.333 66.667 50.000 57.143 42.857 57.143 50.000 83.333 50.000 83.333 83.333 83.333 50.000 57.143 42.857 71.429 83.333 50.000 50.000 33.333 66.667 66.667 33.333 57.143 100.000 57.143 83.333 83.333 83.333 66.667 50.000 66.667 83.333 71.429 85.714 71.429 66.667 66.667 66.667 50.000 66.667 66.667 66.667 85.714 71.429 42.857 66.667 83.333 83.333 50.000 83.333 83.333 33.333 wine 94.444 100.000 94.444 94.444 100.000 100.000 100.000 100.000 100.000 100.000 88.889 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 100.000 100.000 88.889 100.000 94.444 100.000 94.444 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 94.444 94.444 100.000 100.000 100.000 100.000 94.444 100.000 94.444 100.000 100.000 100.000 94.118 100.000 100.000 94.444 100.000 94.444 100.000 94.444 100.000 100.000 100.000 100.000 94.444 100.000 94.444 94.444 100.000 100.000 100.000 100.000 94.118 100.000 100.000 100.000 100.000 100.000 94.444 100.000 100.000 100.000 100.000 88.235 100.000 100.000 100.000 94.444 100.000 88.889 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 94.444 100.000 94.118 100.000 wisconsin-breast-cancer 100.000 95.714 97.143 92.857 95.714 100.000 98.571 98.571 95.714 95.652 97.143 98.571 95.714 95.714 100.000 95.714 98.571 94.286 97.143 98.551 100.000 94.286 97.143 98.571 94.286 97.143 98.571 97.143 95.714 97.101 98.571 94.286 97.143 98.571 97.143 94.286 97.143 100.000 95.714 97.101 97.143 94.286 97.143 94.286 97.143 97.143 97.143 98.571 97.143 98.551 97.143 98.571 97.143 95.714 95.714 97.143 100.000 94.286 95.714 100.000 95.714 95.714 97.143 94.286 98.571 98.571 98.571 97.143 94.286 100.000 97.143 100.000 98.571 95.714 94.286 97.143 95.714 95.714 100.000 98.551 97.143 98.571 97.143 92.857 97.143 97.143 97.143 98.571 100.000 94.203 94.286 95.714 100.000 98.571 92.857 98.571 98.571 98.571 97.143 97.101 yeast 57.047 55.034 59.060 58.389 54.730 61.486 61.486 58.108 48.649 53.378 59.732 62.416 57.047 54.362 66.892 51.351 48.649 56.757 57.432 66.892 58.389 62.416 57.718 56.376 65.541 58.784 56.081 53.378 58.784 59.459 58.389 59.732 53.691 60.403 54.054 60.135 60.811 54.054 59.459 59.459 57.047 61.074 63.087 55.705 56.081 54.054 58.108 59.459 55.405 53.378 51.007 57.718 59.732 61.074 55.405 65.541 59.459 62.838 54.054 57.432 59.732 57.047 57.047 55.034 61.486 62.162 54.054 58.784 58.108 50.676 55.034 59.732 60.403 53.691 59.459 55.405 56.757 59.459 52.703 62.838 51.007 57.718 61.745 59.732 56.757 62.162 54.054 57.432 66.892 55.405 58.389 57.718 53.020 60.403 57.432 58.108 60.811 60.135 54.054 55.405 zoo 90.909 100.000 100.000 90.000 90.000 100.000 90.000 100.000 90.000 100.000 90.909 90.000 90.000 100.000 100.000 90.000 100.000 100.000 100.000 90.000 90.909 100.000 100.000 90.000 90.000 100.000 90.000 90.000 100.000 100.000 90.909 100.000 100.000 90.000 100.000 80.000 100.000 90.000 90.000 100.000 100.000 100.000 80.000 100.000 90.000 100.000 80.000 90.000 100.000 100.000 90.909 100.000 90.000 100.000 100.000 80.000 90.000 100.000 100.000 100.000 100.000 90.000 100.000 100.000 90.000 100.000 100.000 90.000 100.000 90.000 100.000 100.000 90.000 90.000 100.000 80.000 100.000 80.000 100.000 100.000 90.909 100.000 100.000 90.000 100.000 90.000 80.000 90.000 100.000 100.000 90.909 100.000 90.000 80.000 100.000 100.000 100.000 90.000 90.000 100.000 hnb anneal 96.667 97.778 96.667 100.000 98.889 97.778 97.778 97.778 97.753 98.876 98.889 100.000 100.000 98.889 97.778 97.778 96.667 95.556 96.629 100.000 98.889 98.889 98.889 98.889 97.778 98.889 97.778 98.889 95.506 97.753 97.778 98.889 96.667 96.667 95.556 97.778 100.000 98.889 100.000 100.000 97.778 98.889 96.667 97.778 100.000 98.889 98.889 98.889 98.876 97.753 97.778 96.667 100.000 100.000 97.778 96.667 98.889 97.778 100.000 100.000 98.889 98.889 97.778 98.889 97.778 96.667 98.889 98.889 95.506 98.876 98.889 96.667 97.778 97.778 97.778 98.889 96.667 98.889 97.753 100.000 97.778 96.667 100.000 100.000 97.778 100.000 93.333 96.667 97.753 98.876 98.889 100.000 97.778 98.889 95.556 97.778 97.778 98.889 98.876 97.753 audiology 73.913 78.261 69.565 69.565 69.565 86.957 72.727 77.273 72.727 63.636 69.565 78.261 73.913 73.913 65.217 69.565 77.273 77.273 72.727 77.273 73.913 69.565 73.913 82.609 73.913 69.565 72.727 68.182 72.727 72.727 69.565 73.913 60.870 69.565 73.913 82.609 90.909 68.182 68.182 81.818 73.913 69.565 78.261 78.261 82.609 65.217 77.273 63.636 68.182 63.636 82.609 78.261 78.261 65.217 69.565 73.913 72.727 77.273 63.636 68.182 65.217 78.261 73.913 65.217 73.913 78.261 77.273 68.182 77.273 63.636 69.565 69.565 82.609 65.217 73.913 65.217 77.273 86.364 68.182 77.273 69.565 73.913 73.913 73.913 73.913 73.913 72.727 81.818 77.273 68.182 78.261 69.565 73.913 78.261 60.870 65.217 81.818 81.818 72.727 68.182 cleeland-14 83.871 87.097 87.097 86.667 76.667 86.667 86.667 76.667 70.000 73.333 83.871 90.323 70.968 86.667 93.333 73.333 86.667 80.000 76.667 83.333 87.097 80.645 87.097 76.667 83.333 70.000 76.667 86.667 80.000 76.667 87.097 64.516 93.548 90.000 96.667 70.000 83.333 76.667 80.000 80.000 77.419 87.097 70.968 100.000 70.000 86.667 90.000 90.000 70.000 93.333 77.419 74.194 77.419 76.667 90.000 83.333 80.000 83.333 90.000 90.000 90.323 77.419 70.968 73.333 93.333 80.000 90.000 83.333 83.333 83.333 77.419 83.871 83.871 90.000 93.333 66.667 70.000 73.333 80.000 86.667 77.419 90.323 80.645 76.667 86.667 80.000 73.333 90.000 86.667 76.667 87.097 87.097 74.194 83.333 83.333 80.000 86.667 76.667 83.333 80.000 cmc 56.081 54.730 59.459 57.823 48.299 57.823 50.340 51.020 48.299 44.218 52.703 47.973 50.000 55.782 50.340 57.143 44.898 54.422 52.381 48.980 51.351 48.649 61.486 50.340 46.939 49.660 51.701 53.741 52.381 57.823 52.027 54.730 54.054 49.660 47.619 54.422 56.463 47.619 46.939 52.381 51.351 50.676 46.622 54.422 52.381 50.340 55.102 51.701 51.020 51.701 56.081 46.622 52.027 51.701 56.463 54.422 51.020 47.619 46.939 54.422 50.000 56.081 53.378 47.619 52.381 44.898 53.061 52.381 49.660 56.463 56.757 47.973 41.892 55.102 53.061 54.422 48.980 59.184 49.660 51.701 49.324 45.270 55.405 43.537 50.340 46.259 53.741 52.381 54.422 51.701 58.108 49.324 47.973 48.980 47.619 54.422 56.463 44.898 55.102 55.782 contact-lenses 66.667 66.667 66.667 33.333 50.000 100.000 100.000 100.000 50.000 50.000 33.333 66.667 33.333 66.667 100.000 50.000 0.000 50.000 50.000 100.000 66.667 0.000 66.667 100.000 50.000 50.000 50.000 50.000 50.000 50.000 33.333 66.667 66.667 100.000 100.000 50.000 50.000 50.000 50.000 0.000 33.333 33.333 33.333 33.333 100.000 100.000 100.000 0.000 100.000 50.000 66.667 33.333 33.333 33.333 50.000 100.000 50.000 100.000 100.000 100.000 66.667 66.667 66.667 100.000 100.000 0.000 100.000 50.000 50.000 50.000 66.667 66.667 100.000 66.667 100.000 50.000 0.000 50.000 50.000 50.000 100.000 100.000 33.333 66.667 100.000 50.000 50.000 0.000 50.000 50.000 66.667 33.333 33.333 33.333 100.000 50.000 100.000 100.000 100.000 50.000 credit 84.058 82.609 84.058 84.058 84.058 85.507 88.406 92.754 84.058 76.812 88.406 86.957 84.058 85.507 82.609 88.406 84.058 84.058 85.507 81.159 85.507 88.406 86.957 84.058 88.406 76.812 84.058 88.406 85.507 78.261 79.710 88.406 82.609 86.957 85.507 81.159 92.754 86.957 89.855 85.507 75.362 85.507 82.609 91.304 82.609 84.058 85.507 85.507 84.058 88.406 84.058 76.812 78.261 85.507 86.957 85.507 84.058 91.304 85.507 89.855 84.058 84.058 84.058 85.507 88.406 84.058 86.957 84.058 92.754 79.710 86.957 86.957 86.957 86.957 84.058 85.507 82.609 79.710 88.406 84.058 88.406 91.304 81.159 79.710 81.159 82.609 92.754 89.855 79.710 76.812 84.058 91.304 89.855 85.507 89.855 78.261 76.812 89.855 73.913 84.058 credit 84.058 82.609 84.058 84.058 84.058 85.507 88.406 92.754 84.058 76.812 88.406 86.957 84.058 85.507 82.609 88.406 84.058 84.058 85.507 81.159 85.507 88.406 86.957 84.058 88.406 76.812 84.058 88.406 85.507 78.261 79.710 88.406 82.609 86.957 85.507 81.159 92.754 86.957 89.855 85.507 75.362 85.507 82.609 91.304 82.609 84.058 85.507 85.507 84.058 88.406 84.058 76.812 78.261 85.507 86.957 85.507 84.058 91.304 85.507 89.855 84.058 84.058 84.058 85.507 88.406 84.058 86.957 84.058 92.754 79.710 86.957 86.957 86.957 86.957 84.058 85.507 82.609 79.710 88.406 84.058 88.406 91.304 81.159 79.710 81.159 82.609 92.754 89.855 79.710 76.812 84.058 91.304 89.855 85.507 89.855 78.261 76.812 89.855 73.913 84.058 ecoli 79.412 76.471 85.294 82.353 82.353 82.353 84.848 69.697 78.788 78.788 76.471 82.353 88.235 88.235 70.588 79.412 78.788 78.788 81.818 78.788 79.412 82.353 76.471 88.235 82.353 79.412 84.848 81.818 87.879 84.848 73.529 82.353 73.529 79.412 79.412 82.353 78.788 78.788 84.848 84.848 76.471 88.235 79.412 85.294 88.235 73.529 87.879 69.697 81.818 78.788 82.353 82.353 73.529 82.353 79.412 79.412 78.788 81.818 84.848 81.818 85.294 76.471 79.412 73.529 76.471 79.412 84.848 72.727 84.848 75.758 76.471 73.529 79.412 82.353 88.235 79.412 90.909 75.758 75.758 78.788 76.471 67.647 73.529 82.353 85.294 85.294 78.788 93.939 78.788 78.788 85.294 67.647 82.353 73.529 82.353 79.412 69.697 72.727 84.848 78.788 eucalyptus 56.757 62.162 64.865 60.811 59.459 62.162 76.712 63.014 67.123 58.904 56.757 62.162 68.919 60.811 71.622 66.216 61.644 68.493 69.863 63.014 60.811 50.000 64.865 63.514 68.919 59.459 58.904 72.603 64.384 68.493 63.514 60.811 67.568 55.405 62.162 63.514 68.493 65.753 63.014 65.753 64.865 68.919 58.108 68.919 63.514 63.514 69.863 53.425 52.055 65.753 58.108 66.216 67.568 55.405 56.757 66.216 67.123 73.973 60.274 63.014 66.216 62.162 66.216 58.108 62.162 66.216 60.274 49.315 64.384 69.863 60.811 62.162 60.811 64.865 71.622 62.162 63.014 60.274 69.863 65.753 67.568 60.811 71.622 70.270 59.459 66.216 54.795 63.014 68.493 52.055 59.459 63.514 62.162 60.811 68.919 67.568 63.014 69.863 54.795 56.164 german-credit 76.000 69.000 79.000 77.000 75.000 75.000 79.000 74.000 81.000 81.000 80.000 77.000 76.000 74.000 71.000 75.000 77.000 76.000 78.000 77.000 77.000 74.000 75.000 81.000 79.000 73.000 69.000 73.000 70.000 77.000 73.000 73.000 76.000 75.000 79.000 78.000 77.000 82.000 69.000 76.000 79.000 71.000 71.000 77.000 75.000 74.000 73.000 76.000 81.000 76.000 75.000 76.000 73.000 78.000 73.000 74.000 75.000 76.000 80.000 75.000 73.000 70.000 76.000 79.000 80.000 82.000 76.000 68.000 77.000 73.000 71.000 71.000 79.000 74.000 76.000 76.000 72.000 73.000 80.000 82.000 73.000 83.000 77.000 72.000 74.000 80.000 85.000 73.000 74.000 67.000 76.000 74.000 76.000 75.000 75.000 81.000 70.000 75.000 79.000 77.000 glass 63.636 81.818 81.818 77.273 66.667 80.952 57.143 80.952 71.429 80.952 68.182 86.364 86.364 54.545 76.190 71.429 80.952 71.429 66.667 76.190 81.818 81.818 81.818 63.636 66.667 61.905 71.429 90.476 66.667 71.429 68.182 72.727 68.182 54.545 71.429 85.714 47.619 85.714 76.190 85.714 63.636 63.636 77.273 72.727 80.952 80.952 61.905 76.190 76.190 76.190 86.364 77.273 72.727 72.727 85.714 71.429 57.143 61.905 90.476 61.905 63.636 72.727 81.818 77.273 85.714 80.952 80.952 61.905 71.429 80.952 63.636 77.273 95.455 63.636 71.429 61.905 85.714 66.667 76.190 80.952 72.727 63.636 86.364 81.818 71.429 76.190 66.667 71.429 61.905 80.952 95.455 86.364 72.727 68.182 100.000 66.667 76.190 47.619 61.905 66.667 grub-damage 31.250 43.750 37.500 31.250 25.000 46.667 26.667 46.667 33.333 46.667 31.250 37.500 43.750 43.750 43.750 33.333 33.333 46.667 40.000 26.667 31.250 31.250 50.000 37.500 56.250 60.000 33.333 40.000 33.333 40.000 25.000 31.250 25.000 31.250 37.500 33.333 46.667 60.000 46.667 40.000 31.250 37.500 50.000 56.250 37.500 33.333 20.000 40.000 40.000 40.000 31.250 37.500 43.750 56.250 50.000 20.000 40.000 46.667 33.333 40.000 37.500 18.750 31.250 43.750 31.250 33.333 46.667 40.000 46.667 46.667 12.500 43.750 31.250 43.750 43.750 40.000 53.333 26.667 46.667 46.667 43.750 43.750 56.250 37.500 43.750 40.000 33.333 26.667 20.000 26.667 31.250 50.000 25.000 25.000 50.000 40.000 40.000 60.000 40.000 46.667 haberman 74.194 77.419 77.419 74.194 70.968 67.742 66.667 66.667 63.333 76.667 70.968 80.645 70.968 74.194 74.194 70.968 76.667 70.000 83.333 70.000 67.742 77.419 67.742 74.194 77.419 64.516 73.333 80.000 56.667 66.667 64.516 77.419 80.645 74.194 77.419 70.968 76.667 73.333 73.333 60.000 74.194 61.290 67.742 61.290 83.871 74.194 73.333 70.000 76.667 70.000 74.194 64.516 77.419 80.645 77.419 70.968 80.000 76.667 70.000 63.333 67.742 77.419 77.419 70.968 74.194 67.742 66.667 73.333 56.667 83.333 61.290 61.290 70.968 67.742 77.419 74.194 76.667 76.667 80.000 63.333 70.968 74.194 74.194 70.968 70.968 77.419 66.667 63.333 73.333 83.333 67.742 54.839 74.194 80.645 77.419 74.194 73.333 86.667 80.000 73.333 hayes-roth 62.500 62.500 56.250 62.500 62.500 50.000 56.250 50.000 43.750 62.500 56.250 50.000 56.250 56.250 56.250 62.500 62.500 62.500 50.000 62.500 50.000 56.250 56.250 50.000 62.500 56.250 56.250 62.500 62.500 62.500 50.000 56.250 56.250 56.250 62.500 62.500 62.500 62.500 56.250 56.250 62.500 62.500 56.250 62.500 50.000 50.000 56.250 50.000 56.250 56.250 62.500 62.500 56.250 43.750 62.500 56.250 56.250 56.250 56.250 62.500 50.000 50.000 56.250 56.250 62.500 62.500 56.250 56.250 62.500 50.000 56.250 62.500 56.250 56.250 62.500 50.000 50.000 56.250 56.250 62.500 50.000 56.250 50.000 56.250 56.250 62.500 62.500 62.500 43.750 62.500 56.250 62.500 62.500 50.000 62.500 62.500 50.000 56.250 50.000 50.000 hepatitis 100.000 87.500 75.000 81.250 87.500 86.667 73.333 86.667 80.000 93.333 87.500 93.750 100.000 68.750 87.500 80.000 100.000 60.000 86.667 100.000 87.500 68.750 87.500 81.250 93.750 80.000 93.333 93.333 73.333 93.333 68.750 81.250 87.500 75.000 75.000 93.333 86.667 86.667 93.333 86.667 93.750 81.250 81.250 100.000 75.000 93.333 86.667 73.333 66.667 93.333 87.500 93.750 87.500 81.250 87.500 80.000 93.333 86.667 86.667 73.333 81.250 93.750 87.500 87.500 87.500 80.000 66.667 86.667 93.333 93.333 87.500 100.000 75.000 75.000 93.750 80.000 86.667 93.333 100.000 93.333 81.250 93.750 75.000 87.500 87.500 86.667 80.000 80.000 80.000 93.333 93.750 87.500 81.250 93.750 81.250 86.667 73.333 93.333 80.000 93.333 hungarian-14 76.667 76.667 80.000 93.333 89.655 86.207 79.310 89.655 86.207 86.207 80.000 90.000 93.333 86.667 86.207 79.310 72.414 82.759 93.103 86.207 83.333 80.000 90.000 80.000 89.655 82.759 86.207 100.000 75.862 82.759 83.333 80.000 83.333 80.000 75.862 96.552 75.862 89.655 93.103 93.103 86.667 86.667 83.333 76.667 89.655 86.207 75.862 82.759 93.103 93.103 80.000 83.333 86.667 80.000 86.207 89.655 75.862 86.207 82.759 89.655 90.000 86.667 90.000 90.000 82.759 86.207 82.759 89.655 75.862 86.207 80.000 76.667 90.000 90.000 75.862 93.103 93.103 79.310 82.759 86.207 90.000 83.333 76.667 86.667 72.414 82.759 82.759 79.310 93.103 89.655 76.667 83.333 83.333 96.667 89.655 89.655 86.207 82.759 75.862 82.759 hypothyroid 98.942 98.677 99.204 99.469 99.204 98.939 99.469 99.204 99.469 99.204 99.206 99.471 98.939 98.674 98.939 99.469 99.469 97.613 99.469 98.674 98.942 99.471 98.674 99.204 98.674 98.939 98.674 99.204 99.204 99.735 98.413 98.148 99.469 99.469 98.939 99.204 99.735 98.143 100.000 98.939 98.413 98.942 98.939 99.469 98.143 98.939 99.735 98.939 99.735 98.939 98.942 98.942 98.674 100.000 99.735 98.674 99.469 98.939 98.143 99.204 99.471 98.942 98.674 98.674 99.469 99.735 99.469 98.939 98.408 98.939 98.413 98.148 99.204 100.000 98.939 99.204 98.674 99.735 98.939 99.469 98.942 98.413 100.000 98.143 99.469 98.939 99.469 99.469 98.674 99.735 99.735 98.413 99.204 98.939 98.939 98.143 99.204 98.939 98.408 99.204 ionosphere 91.667 85.714 94.286 91.429 97.143 91.429 94.286 85.714 91.429 85.714 91.667 82.857 94.286 85.714 97.143 91.429 94.286 100.000 97.143 82.857 91.667 97.143 94.286 82.857 97.143 77.143 97.143 91.429 88.571 97.143 94.444 88.571 97.143 97.143 88.571 85.714 91.429 91.429 88.571 88.571 88.889 97.143 88.571 97.143 88.571 94.286 94.286 88.571 85.714 94.286 88.889 85.714 91.429 91.429 94.286 100.000 94.286 94.286 91.429 91.429 91.667 82.857 88.571 91.429 94.286 97.143 85.714 88.571 94.286 97.143 91.667 91.429 85.714 91.429 85.714 94.286 88.571 97.143 91.429 91.429 88.889 91.429 88.571 94.286 85.714 97.143 88.571 88.571 94.286 100.000 97.222 97.143 91.429 88.571 91.429 85.714 91.429 94.286 85.714 94.286 iris 93.333 93.333 100.000 100.000 86.667 93.333 93.333 80.000 93.333 86.667 93.333 73.333 93.333 100.000 100.000 93.333 93.333 93.333 86.667 100.000 100.000 86.667 86.667 93.333 86.667 100.000 86.667 93.333 93.333 86.667 86.667 93.333 80.000 93.333 93.333 93.333 93.333 86.667 86.667 100.000 86.667 73.333 93.333 100.000 93.333 100.000 100.000 93.333 93.333 93.333 93.333 93.333 93.333 86.667 100.000 93.333 93.333 93.333 100.000 93.333 86.667 86.667 93.333 93.333 93.333 86.667 93.333 93.333 93.333 93.333 80.000 86.667 93.333 93.333 100.000 80.000 93.333 100.000 80.000 93.333 100.000 100.000 80.000 93.333 93.333 100.000 93.333 93.333 93.333 93.333 93.333 93.333 100.000 80.000 93.333 100.000 93.333 80.000 93.333 93.333 kr-s-kp 93.125 89.688 94.062 92.812 91.562 91.250 93.730 91.850 92.790 93.730 93.438 91.250 93.125 91.875 93.438 90.625 91.536 91.850 91.536 93.417 90.625 91.875 91.250 90.938 92.188 92.500 93.730 94.044 91.850 95.298 94.688 91.562 91.250 92.812 92.812 93.438 91.536 91.850 91.536 93.103 91.250 94.375 91.250 91.250 91.562 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83.333 83.333 83.333 100.000 100.000 100.000 100.000 80.000 100.000 80.000 100.000 100.000 100.000 100.000 83.333 83.333 100.000 60.000 100.000 100.000 lier-disorders 48.571 57.143 54.286 57.143 57.143 58.824 58.824 55.882 52.941 61.765 57.143 57.143 57.143 57.143 57.143 55.882 55.882 58.824 50.000 58.824 51.429 51.429 62.857 54.286 57.143 58.824 58.824 58.824 55.882 58.824 54.286 62.857 57.143 34.286 62.857 58.824 55.882 58.824 58.824 52.941 51.429 57.143 57.143 60.000 57.143 55.882 58.824 61.765 58.824 50.000 51.429 60.000 42.857 57.143 57.143 58.824 58.824 58.824 50.000 58.824 60.000 57.143 54.286 57.143 57.143 38.235 58.824 55.882 58.824 58.824 54.286 60.000 57.143 57.143 57.143 58.824 61.765 61.765 52.941 58.824 45.714 57.143 51.429 60.000 57.143 58.824 58.824 58.824 61.765 58.824 62.857 62.857 57.143 62.857 48.571 58.824 58.824 50.000 61.765 58.824 lymphography 93.333 86.667 86.667 80.000 86.667 86.667 86.667 93.333 78.571 71.429 93.333 93.333 80.000 86.667 86.667 93.333 66.667 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70.000 58.333 63.934 66.667 63.333 63.333 66.667 68.333 66.667 70.000 58.333 70.000 68.852 65.000 66.667 65.000 71.667 68.333 71.667 65.000 66.667 65.000 63.934 68.333 70.000 68.333 61.667 61.667 66.667 66.667 63.333 65.000 59.016 71.667 71.667 71.667 66.667 73.333 70.000 58.333 76.667 65.000 monks1 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 monks3 94.643 100.000 96.429 96.429 100.000 100.000 100.000 96.364 100.000 94.545 96.429 94.643 98.214 100.000 98.182 98.182 98.182 98.182 100.000 98.182 100.000 98.214 100.000 96.429 96.364 100.000 98.182 98.182 98.182 96.364 100.000 98.214 98.214 100.000 96.364 94.545 100.000 98.182 96.364 96.364 98.214 100.000 98.214 96.429 96.364 98.182 98.182 98.182 98.182 98.182 100.000 98.214 96.429 100.000 100.000 100.000 98.182 96.364 98.182 96.364 92.857 98.214 100.000 96.429 96.364 100.000 92.727 100.000 100.000 98.182 96.429 100.000 100.000 98.214 98.182 100.000 98.182 98.182 96.364 96.364 98.214 100.000 100.000 94.643 98.182 98.182 98.182 98.182 96.364 100.000 96.429 100.000 98.214 100.000 98.182 96.364 100.000 96.364 96.364 96.364 mushroom 99.877 99.877 100.000 100.000 99.877 100.000 100.000 100.000 100.000 100.000 99.877 100.000 99.877 100.000 100.000 100.000 100.000 99.877 100.000 100.000 100.000 100.000 99.877 100.000 100.000 100.000 100.000 99.877 99.877 100.000 100.000 100.000 100.000 99.877 99.877 100.000 99.877 100.000 100.000 100.000 100.000 100.000 99.754 100.000 100.000 100.000 100.000 99.877 100.000 100.000 99.877 100.000 100.000 99.877 100.000 100.000 99.877 100.000 100.000 100.000 99.877 100.000 100.000 100.000 100.000 99.877 100.000 100.000 99.877 100.000 100.000 100.000 100.000 99.877 100.000 99.877 100.000 99.877 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 99.877 100.000 99.877 99.877 100.000 99.877 100.000 99.877 100.000 99.877 100.000 100.000 100.000 100.000 nursery 94.213 94.444 94.444 93.904 95.370 93.827 94.136 94.290 93.750 94.444 94.213 93.981 94.059 94.290 94.599 94.444 93.904 94.213 94.522 94.522 94.059 94.059 94.522 94.444 94.213 93.904 94.290 93.904 95.139 93.981 94.676 95.525 93.287 94.676 93.750 93.364 94.599 94.290 93.904 94.290 94.367 94.444 94.213 94.213 94.290 93.827 94.522 94.290 94.136 94.290 94.290 93.364 94.444 94.599 94.985 94.753 93.981 94.599 94.676 93.287 94.753 93.596 94.136 94.367 95.216 94.676 93.904 94.059 93.287 94.599 94.522 92.670 93.441 94.367 94.522 93.210 95.062 95.370 94.444 94.136 93.981 93.596 94.522 93.673 94.676 93.827 95.293 93.981 94.213 93.904 94.444 94.444 94.522 94.059 93.596 93.827 93.981 94.367 94.522 94.985 optdigits 95.907 96.441 96.085 95.907 95.907 95.018 97.331 96.797 96.441 95.907 96.619 96.975 96.797 94.306 96.797 97.153 96.797 94.662 94.662 97.153 95.196 96.619 96.797 96.619 97.509 95.374 94.484 96.441 97.331 96.975 95.730 95.196 94.662 96.441 97.865 97.687 94.840 97.331 96.797 95.374 95.374 96.975 95.730 97.331 95.907 95.196 95.907 96.441 95.552 96.619 95.730 96.263 96.085 95.907 96.797 95.907 96.441 96.797 95.552 96.797 95.196 97.331 95.018 96.441 96.441 96.085 96.263 98.043 96.263 96.441 95.907 96.263 95.907 95.730 96.441 95.374 96.797 96.797 95.374 97.331 95.907 95.552 95.730 95.552 96.975 96.975 95.730 96.263 96.619 97.509 95.730 95.018 95.907 94.306 97.509 96.263 96.619 97.331 96.975 95.196 owel 96.168 97.445 96.168 96.527 96.161 97.258 97.075 96.892 96.892 97.806 97.080 95.803 96.168 96.527 96.161 96.709 97.075 96.892 96.527 95.978 97.080 95.985 95.985 96.161 97.441 95.795 95.978 96.892 97.075 97.441 97.445 97.445 96.898 95.795 97.258 97.075 96.709 97.075 97.258 94.516 97.263 96.715 96.533 96.892 94.881 97.441 97.441 96.161 95.612 97.441 97.810 96.168 95.438 97.258 95.064 95.430 97.258 97.623 97.806 97.441 96.533 97.993 96.533 97.806 95.795 96.161 96.527 96.344 97.258 96.344 96.168 97.263 97.628 95.978 96.892 96.161 96.892 96.161 96.344 96.709 96.715 97.080 97.080 96.527 95.612 95.978 96.709 96.709 96.344 98.172 95.255 97.445 96.898 96.709 96.892 96.709 95.612 96.892 97.623 96.527 page-blocks 75.000 100.000 100.000 100.000 75.000 75.000 66.667 100.000 100.000 66.667 75.000 100.000 25.000 75.000 100.000 100.000 66.667 100.000 100.000 100.000 50.000 50.000 100.000 75.000 75.000 100.000 66.667 100.000 100.000 100.000 100.000 75.000 75.000 100.000 75.000 75.000 66.667 100.000 100.000 66.667 75.000 75.000 75.000 50.000 75.000 75.000 100.000 100.000 66.667 100.000 75.000 100.000 100.000 75.000 100.000 100.000 66.667 100.000 100.000 66.667 100.000 75.000 75.000 75.000 100.000 75.000 100.000 100.000 66.667 100.000 50.000 75.000 100.000 100.000 100.000 50.000 100.000 66.667 66.667 100.000 100.000 75.000 75.000 100.000 75.000 75.000 100.000 66.667 100.000 66.667 75.000 75.000 75.000 100.000 25.000 100.000 100.000 66.667 100.000 66.667 pasture-production 96.727 98.000 97.816 97.179 98.180 97.452 97.907 97.725 97.543 97.543 97.909 97.818 97.816 97.270 98.271 97.907 97.907 97.634 97.725 96.815 98.273 97.636 97.179 96.815 97.998 97.543 97.725 97.816 98.453 98.271 97.182 97.364 97.998 97.543 96.997 97.816 98.089 97.452 98.271 97.270 98.000 98.636 97.270 97.361 97.270 97.179 97.907 97.907 97.634 97.452 97.455 97.636 97.452 97.725 98.362 96.815 97.998 97.816 97.816 98.271 97.364 97.636 97.725 97.725 97.088 97.816 98.362 97.816 97.998 97.907 97.636 97.909 97.998 98.180 96.997 97.816 97.361 97.907 97.452 96.724 97.909 97.545 98.089 97.634 97.634 97.543 97.179 98.089 97.361 97.998 97.455 97.545 97.543 97.634 96.724 97.816 97.634 98.180 97.361 97.725 pendigits 75.325 77.922 76.623 75.325 76.623 68.831 67.532 71.429 73.684 77.632 74.026 71.429 76.623 75.325 77.922 80.519 79.221 70.130 82.895 75.000 70.130 68.831 77.922 77.922 80.519 68.831 77.922 74.026 75.000 80.263 79.221 80.519 70.130 66.234 72.727 75.325 83.117 77.922 72.368 73.684 84.416 72.727 76.623 77.922 80.519 70.130 68.831 72.727 68.421 75.000 74.026 74.026 72.727 75.325 71.429 76.623 70.130 81.818 81.579 61.842 72.727 76.623 74.026 64.935 76.623 74.026 80.519 72.727 80.263 78.947 83.117 75.325 70.130 75.325 71.429 77.922 70.130 67.532 73.684 75.000 77.922 76.623 72.727 74.026 63.636 72.727 76.623 81.818 78.947 68.421 72.727 71.429 77.922 71.429 75.325 71.429 77.922 76.623 71.053 63.158 pima-diabetes 66.667 66.667 66.667 66.667 66.667 77.778 77.778 66.667 66.667 55.556 66.667 66.667 55.556 88.889 66.667 55.556 66.667 55.556 44.444 55.556 77.778 77.778 77.778 66.667 55.556 66.667 55.556 66.667 55.556 66.667 66.667 77.778 55.556 77.778 66.667 66.667 66.667 66.667 66.667 55.556 66.667 66.667 55.556 44.444 77.778 66.667 66.667 55.556 55.556 55.556 88.889 66.667 77.778 66.667 66.667 66.667 33.333 66.667 66.667 55.556 88.889 77.778 66.667 66.667 44.444 66.667 66.667 66.667 66.667 55.556 55.556 66.667 77.778 55.556 55.556 55.556 77.778 66.667 66.667 66.667 77.778 55.556 44.444 66.667 55.556 44.444 66.667 77.778 77.778 77.778 55.556 66.667 66.667 66.667 66.667 66.667 55.556 66.667 66.667 77.778 postoperatie 55.882 50.000 47.059 41.176 44.118 52.941 50.000 44.118 47.059 48.485 41.176 41.176 44.118 44.118 52.941 52.941 52.941 41.176 47.059 54.545 55.882 50.000 44.118 55.882 47.059 50.000 50.000 44.118 38.235 42.424 50.000 55.882 44.118 50.000 64.706 44.118 50.000 44.118 47.059 39.394 44.118 55.882 55.882 61.765 38.235 47.059 50.000 47.059 38.235 45.455 47.059 41.176 35.294 52.941 44.118 50.000 41.176 52.941 47.059 57.576 38.235 50.000 47.059 55.882 44.118 50.000 55.882 44.118 44.118 54.545 50.000 50.000 50.000 55.882 41.176 50.000 44.118 38.235 55.882 42.424 44.118 47.059 52.941 47.059 52.941 47.059 35.294 58.824 41.176 51.515 47.059 52.941 58.824 44.118 38.235 55.882 44.118 44.118 47.059 39.394 primary-tumor 95.238 95.238 96.970 96.104 97.403 94.805 95.671 98.268 96.104 97.835 96.104 95.671 97.835 96.104 96.104 97.403 96.970 96.537 97.835 97.835 96.537 98.268 96.970 98.268 95.671 96.537 95.671 94.805 97.835 94.805 96.104 97.403 95.671 97.835 96.104 95.671 96.104 96.970 96.970 97.403 96.104 95.238 97.403 98.701 95.671 95.671 96.104 96.970 96.537 96.537 98.268 96.970 96.104 98.268 96.537 93.939 96.970 96.104 95.671 96.104 96.104 96.970 96.970 97.403 95.671 97.403 96.970 94.372 96.537 95.238 95.671 96.970 94.372 96.970 99.134 96.970 95.671 95.671 96.970 96.104 97.835 96.104 93.074 96.970 95.671 96.970 97.835 95.671 96.970 97.403 96.970 94.805 95.238 95.238 96.970 96.104 95.238 95.671 97.403 97.403 segment 87.879 87.879 87.879 87.500 87.500 93.750 87.500 87.500 84.375 90.625 87.879 90.909 84.848 93.750 93.750 93.750 87.500 87.500 81.250 87.500 93.939 84.848 87.879 81.250 90.625 96.875 93.750 87.500 87.500 84.375 90.909 90.909 81.818 96.875 93.750 87.500 87.500 87.500 87.500 84.375 87.879 84.848 84.848 90.625 90.625 87.500 87.500 87.500 93.750 90.625 87.879 90.909 90.909 87.500 87.500 87.500 93.750 87.500 84.375 87.500 84.848 87.879 90.909 87.500 87.500 87.500 87.500 93.750 87.500 90.625 84.848 84.848 84.848 93.750 96.875 93.750 87.500 81.250 87.500 87.500 90.909 87.879 84.848 90.625 90.625 87.500 90.625 87.500 84.375 90.625 87.879 90.909 87.879 90.625 87.500 90.625 87.500 87.500 90.625 87.500 solar-flare-C 90.909 75.758 87.879 84.375 87.500 90.625 87.500 87.500 90.625 87.500 93.939 87.879 87.879 87.500 87.500 84.375 84.375 87.500 78.125 90.625 90.909 87.879 84.848 87.500 93.750 90.625 84.375 84.375 84.375 90.625 90.909 87.879 87.879 84.375 90.625 87.500 84.375 90.625 90.625 84.375 87.879 90.909 84.848 87.500 90.625 87.500 84.375 87.500 90.625 87.500 87.879 87.879 90.909 84.375 84.375 90.625 90.625 87.500 78.125 93.750 84.848 78.788 87.879 90.625 81.250 90.625 90.625 87.500 84.375 87.500 90.909 78.788 78.788 93.750 90.625 87.500 87.500 87.500 90.625 87.500 84.848 87.879 90.909 84.375 90.625 84.375 87.500 84.375 87.500 90.625 90.909 90.909 87.879 87.500 90.625 87.500 87.500 90.625 81.250 87.500 solar-flare-X 96.970 96.970 96.970 100.000 100.000 100.000 96.875 93.750 96.875 96.875 96.970 90.909 96.970 100.000 96.875 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 96.875 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 93.750 93.750 96.875 96.970 96.970 96.970 100.000 100.000 96.875 96.875 96.875 96.875 96.875 96.970 96.970 93.939 96.875 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 96.875 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 96.875 100.000 96.875 96.875 96.875 96.875 96.970 93.939 96.970 100.000 100.000 100.000 93.750 96.875 96.875 96.875 87.879 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 solar-flare-m 90.476 90.476 71.429 85.714 66.667 66.667 80.952 80.952 85.000 80.000 76.190 66.667 71.429 71.429 61.905 57.143 85.714 61.905 95.000 90.000 80.952 76.190 90.476 90.476 80.952 66.667 57.143 71.429 65.000 75.000 66.667 90.476 71.429 80.952 85.714 71.429 57.143 66.667 90.000 85.000 85.714 76.190 76.190 85.714 71.429 76.190 71.429 90.476 80.000 80.000 85.714 85.714 80.952 66.667 71.429 66.667 85.714 76.190 65.000 55.000 80.952 76.190 66.667 85.714 76.190 90.476 57.143 66.667 80.000 85.000 66.667 85.714 71.429 71.429 61.905 66.667 80.952 80.952 85.000 80.000 71.429 80.952 80.952 85.714 61.905 76.190 71.429 66.667 75.000 85.000 80.952 76.190 85.714 66.667 76.190 76.190 71.429 85.714 60.000 75.000 sonar 94.203 95.652 95.652 94.118 95.588 91.176 98.529 95.588 92.647 92.647 94.203 92.754 91.304 97.059 94.118 97.059 95.588 94.118 91.176 94.118 100.000 94.203 92.754 95.588 91.176 92.647 95.588 95.588 94.118 91.176 94.203 95.652 92.754 97.059 91.176 94.118 95.588 97.059 95.588 94.118 95.652 94.203 94.203 95.588 100.000 91.176 92.647 97.059 92.647 97.059 91.304 97.101 97.101 95.588 94.118 97.059 95.588 94.118 92.647 94.118 89.855 97.101 95.652 94.118 94.118 95.588 94.118 98.529 91.176 97.059 92.754 97.101 97.101 98.529 89.706 98.529 94.118 92.647 92.647 97.059 94.203 95.652 92.754 92.647 98.529 94.118 94.118 92.647 95.588 98.529 92.754 92.754 97.101 97.059 94.118 94.118 95.588 94.118 91.176 95.588 soybean 93.926 92.391 91.522 91.739 92.609 91.087 91.739 92.391 93.478 92.174 91.974 91.087 93.261 92.174 91.522 91.957 95.000 92.174 91.087 92.391 93.059 93.043 92.174 93.261 91.739 92.174 92.826 89.783 91.304 93.913 90.239 92.609 93.261 92.174 92.391 95.652 92.826 91.304 91.522 90.870 91.974 90.000 92.391 90.870 93.043 93.043 94.565 93.261 91.304 91.087 90.022 92.826 92.609 91.957 92.826 92.174 92.174 91.739 92.391 93.696 91.974 93.043 93.696 93.261 91.087 88.261 92.174 91.304 95.000 92.609 91.106 91.739 91.739 92.826 94.348 92.391 91.087 91.522 93.043 92.609 92.191 91.739 93.913 93.913 91.957 92.826 91.739 91.739 91.087 92.826 93.709 92.609 90.870 92.609 91.304 93.478 93.696 90.435 91.957 92.174 spambase 88.889 74.074 74.074 81.481 81.481 81.481 81.481 80.769 88.462 88.462 77.778 77.778 85.185 70.370 74.074 88.889 88.889 84.615 88.462 88.462 85.185 81.481 70.370 85.185 88.889 81.481 74.074 88.462 80.769 80.769 88.889 74.074 81.481 74.074 74.074 92.593 77.778 92.308 80.769 80.769 74.074 81.481 85.185 81.481 92.593 77.778 74.074 80.769 84.615 80.769 74.074 85.185 81.481 81.481 85.185 100.000 74.074 73.077 76.923 84.615 85.185 85.185 81.481 85.185 92.593 74.074 77.778 88.462 76.923 76.923 66.667 85.185 81.481 100.000 77.778 81.481 85.185 84.615 80.769 76.923 92.593 96.296 85.185 81.481 66.667 85.185 77.778 88.462 65.385 76.923 77.778 74.074 96.296 77.778 81.481 66.667 85.185 80.769 84.615 88.462 spect-reordered 95.925 95.298 97.492 96.238 97.806 96.238 95.925 94.357 95.298 97.179 95.925 97.179 95.611 94.357 95.298 97.492 95.298 97.179 96.552 95.925 96.238 96.238 95.925 97.179 96.552 96.552 96.238 96.238 95.611 94.044 94.044 96.865 97.179 96.238 95.611 95.611 97.492 96.552 95.611 95.611 96.552 96.552 96.865 96.865 96.552 95.611 97.492 94.357 94.357 95.611 96.552 94.984 94.357 95.611 95.925 95.925 97.492 96.552 96.552 97.179 96.865 95.298 97.179 95.611 95.925 95.925 95.925 94.357 96.552 98.119 95.925 97.806 95.925 96.238 94.671 94.984 94.984 97.806 96.865 97.179 96.552 94.357 96.552 97.179 95.925 96.238 94.984 96.238 97.179 96.238 98.119 94.671 97.492 95.925 97.492 95.925 95.925 94.044 96.238 95.611 splice 66.667 33.333 60.000 100.000 40.000 40.000 60.000 60.000 40.000 80.000 66.667 33.333 100.000 60.000 40.000 60.000 40.000 40.000 80.000 40.000 66.667 66.667 80.000 40.000 40.000 60.000 100.000 80.000 40.000 40.000 50.000 50.000 60.000 60.000 80.000 60.000 40.000 60.000 40.000 40.000 100.000 66.667 60.000 80.000 40.000 40.000 40.000 80.000 60.000 80.000 33.333 50.000 40.000 80.000 60.000 60.000 60.000 80.000 80.000 20.000 66.667 66.667 60.000 60.000 60.000 60.000 60.000 40.000 60.000 40.000 50.000 33.333 60.000 40.000 60.000 80.000 60.000 80.000 60.000 80.000 33.333 66.667 60.000 60.000 40.000 80.000 40.000 80.000 40.000 40.000 33.333 33.333 60.000 20.000 40.000 40.000 80.000 100.000 80.000 40.000 squash-stored 50.000 100.000 40.000 60.000 80.000 80.000 40.000 80.000 80.000 80.000 66.667 50.000 80.000 80.000 80.000 40.000 100.000 60.000 40.000 60.000 83.333 83.333 100.000 100.000 60.000 40.000 40.000 100.000 100.000 60.000 16.667 83.333 40.000 60.000 80.000 80.000 100.000 80.000 40.000 100.000 33.333 50.000 100.000 80.000 80.000 80.000 80.000 80.000 60.000 80.000 100.000 66.667 80.000 60.000 80.000 60.000 80.000 60.000 80.000 100.000 83.333 66.667 80.000 100.000 40.000 100.000 100.000 60.000 60.000 80.000 50.000 33.333 60.000 80.000 60.000 80.000 60.000 80.000 80.000 100.000 100.000 83.333 60.000 80.000 20.000 80.000 100.000 60.000 60.000 60.000 66.667 33.333 60.000 100.000 80.000 100.000 80.000 60.000 80.000 80.000 squash-unstored 43.750 40.000 53.333 33.333 60.000 40.000 40.000 40.000 40.000 53.333 37.500 46.667 53.333 46.667 46.667 40.000 40.000 26.667 53.333 53.333 43.750 40.000 53.333 33.333 46.667 40.000 46.667 46.667 40.000 53.333 37.500 40.000 46.667 40.000 40.000 53.333 40.000 46.667 46.667 53.333 43.750 40.000 46.667 33.333 40.000 46.667 53.333 46.667 53.333 40.000 31.250 40.000 53.333 40.000 33.333 33.333 46.667 53.333 46.667 40.000 31.250 46.667 40.000 40.000 40.000 53.333 53.333 60.000 40.000 40.000 43.750 33.333 53.333 40.000 53.333 46.667 33.333 46.667 40.000 46.667 50.000 33.333 60.000 33.333 53.333 46.667 40.000 40.000 46.667 40.000 37.500 46.667 46.667 46.667 40.000 33.333 46.667 40.000 46.667 46.667 tae 88.889 88.889 81.818 82.828 88.889 83.838 85.859 76.768 87.879 80.808 88.889 85.859 81.818 89.899 88.889 73.737 82.828 89.899 79.798 89.899 82.828 84.848 86.869 83.838 77.778 85.859 82.828 80.808 83.838 81.818 79.798 87.879 82.828 83.838 87.879 77.778 85.859 91.919 87.879 85.859 81.818 94.949 81.818 87.879 79.798 83.838 84.848 82.828 77.778 89.899 84.848 86.869 81.818 85.859 91.919 84.848 89.899 84.848 87.879 83.838 84.848 86.869 85.859 90.909 77.778 85.859 80.808 82.828 86.869 88.889 83.838 83.838 92.929 83.838 78.788 90.909 78.788 81.818 80.808 92.929 85.859 84.848 81.818 77.778 75.758 86.869 82.828 85.859 88.889 81.818 80.808 91.919 77.778 85.859 76.768 84.848 87.879 88.889 80.808 83.838 waveform 86.000 85.000 83.800 84.400 82.600 85.600 84.600 83.800 82.600 86.800 82.800 85.800 87.400 81.200 86.400 87.400 84.400 85.800 85.400 82.200 85.800 86.200 83.000 86.600 87.800 83.600 84.200 84.200 84.000 83.400 84.800 82.400 85.400 86.400 83.600 83.400 86.200 85.400 86.600 84.800 83.800 85.800 86.200 84.200 87.000 83.600 86.600 84.200 85.600 83.000 84.400 85.400 83.400 84.800 87.000 82.800 85.000 84.400 85.200 86.000 83.800 84.000 83.400 84.000 87.000 85.000 86.800 84.600 84.800 87.200 88.600 84.600 84.400 84.800 83.400 83.400 84.600 84.600 85.800 85.600 85.600 85.000 87.200 86.600 83.400 83.800 87.200 82.600 83.800 84.400 86.000 83.000 83.400 83.600 85.800 85.000 84.600 84.600 86.000 85.200 white-clover 85.714 71.429 85.714 83.333 66.667 83.333 83.333 83.333 66.667 83.333 57.143 42.857 71.429 66.667 100.000 83.333 100.000 83.333 66.667 83.333 42.857 100.000 71.429 100.000 66.667 100.000 83.333 83.333 100.000 66.667 85.714 85.714 71.429 83.333 66.667 66.667 66.667 50.000 83.333 83.333 85.714 100.000 71.429 66.667 66.667 83.333 66.667 83.333 50.000 66.667 71.429 85.714 57.143 66.667 100.000 66.667 100.000 100.000 100.000 66.667 71.429 57.143 85.714 100.000 66.667 100.000 33.333 83.333 66.667 66.667 71.429 85.714 71.429 83.333 83.333 66.667 66.667 83.333 66.667 100.000 71.429 85.714 85.714 83.333 66.667 83.333 66.667 100.000 83.333 100.000 100.000 71.429 57.143 83.333 100.000 100.000 83.333 83.333 83.333 33.333 wine 94.444 100.000 94.444 100.000 100.000 94.444 100.000 100.000 100.000 100.000 88.889 94.444 100.000 100.000 100.000 94.444 100.000 100.000 100.000 100.000 100.000 83.333 100.000 94.444 100.000 94.444 100.000 100.000 100.000 88.235 100.000 100.000 94.444 100.000 94.444 100.000 94.444 94.444 100.000 100.000 100.000 100.000 100.000 100.000 94.444 100.000 94.444 100.000 94.118 100.000 100.000 94.444 100.000 100.000 88.889 94.444 100.000 100.000 100.000 100.000 88.889 100.000 94.444 100.000 100.000 100.000 94.444 100.000 94.118 100.000 100.000 100.000 100.000 100.000 88.889 100.000 100.000 100.000 100.000 94.118 100.000 100.000 100.000 94.444 100.000 83.333 94.444 100.000 100.000 100.000 100.000 100.000 100.000 100.000 94.444 94.444 94.444 100.000 88.235 100.000 wisconsin-breast-cancer 98.571 95.714 97.143 92.857 95.714 98.571 97.143 98.571 97.143 95.652 95.714 97.143 94.286 92.857 98.571 95.714 97.143 95.714 97.143 98.551 97.143 92.857 97.143 97.143 92.857 97.143 98.571 95.714 95.714 95.652 98.571 94.286 97.143 98.571 97.143 92.857 91.429 98.571 95.714 97.101 95.714 95.714 95.714 92.857 98.571 98.571 95.714 95.714 97.143 98.551 95.714 100.000 95.714 95.714 97.143 95.714 97.143 92.857 95.714 100.000 94.286 95.714 94.286 92.857 98.571 98.571 97.143 98.571 92.857 100.000 97.143 98.571 98.571 98.571 94.286 94.286 95.714 94.286 98.571 98.551 97.143 98.571 97.143 92.857 95.714 95.714 95.714 97.143 100.000 94.203 95.714 91.429 98.571 98.571 92.857 94.286 98.571 97.143 95.714 97.101 yeast 60.403 57.718 58.389 60.403 52.703 61.486 61.486 57.432 51.351 53.378 59.060 62.416 57.047 55.034 66.216 51.351 52.027 56.757 56.081 66.216 57.718 59.060 57.047 55.705 64.865 58.784 54.730 51.351 58.108 60.135 59.060 56.376 53.691 63.087 55.405 58.784 61.486 54.054 58.108 60.135 58.389 61.074 63.087 57.047 55.405 54.054 57.432 59.459 54.730 52.027 51.007 57.718 59.060 59.732 54.730 66.892 60.811 61.486 53.378 60.135 60.403 56.376 57.718 55.034 59.459 61.486 57.432 58.108 56.757 51.351 53.691 60.403 60.403 55.705 59.459 56.757 55.405 60.135 54.730 59.459 51.007 59.060 60.403 59.060 56.757 61.486 54.054 55.405 64.189 55.405 59.060 57.718 53.020 59.732 57.432 60.811 57.432 60.811 54.054 54.054 zoo 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 90.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 90.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 90.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 90.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 j48 anneal 96.667 100.000 97.778 100.000 97.778 98.889 98.889 98.889 97.753 97.753 98.889 98.889 98.889 97.778 98.889 98.889 98.889 96.667 100.000 100.000 98.889 97.778 98.889 98.889 93.333 98.889 97.778 98.889 98.876 98.876 97.778 98.889 97.778 97.778 96.667 97.778 100.000 98.889 100.000 98.876 97.778 100.000 97.778 97.778 100.000 98.889 100.000 97.778 98.876 98.876 98.889 100.000 100.000 100.000 96.667 97.778 97.778 96.667 100.000 98.876 98.889 98.889 98.889 98.889 100.000 98.889 98.889 97.778 98.876 98.876 97.778 100.000 100.000 97.778 97.778 100.000 98.889 98.889 96.629 100.000 98.889 97.778 100.000 98.889 98.889 98.889 97.778 97.778 100.000 98.876 100.000 98.889 97.778 100.000 100.000 96.667 97.778 98.889 98.876 98.876 audiology 65.217 86.957 78.261 82.609 73.913 82.609 90.909 77.273 77.273 68.182 86.957 86.957 78.261 69.565 73.913 69.565 72.727 77.273 72.727 77.273 82.609 60.870 78.261 73.913 78.261 73.913 81.818 90.909 68.182 81.818 60.870 78.261 73.913 69.565 73.913 82.609 77.273 95.455 86.364 86.364 78.261 69.565 73.913 78.261 78.261 73.913 72.727 81.818 86.364 77.273 69.565 86.957 78.261 73.913 78.261 78.261 81.818 72.727 63.636 72.727 69.565 69.565 91.304 73.913 86.957 78.261 72.727 81.818 72.727 77.273 73.913 65.217 82.609 78.261 78.261 82.609 90.909 72.727 63.636 77.273 78.261 86.957 82.609 69.565 78.261 78.261 72.727 72.727 77.273 86.364 78.261 82.609 95.652 86.957 52.174 73.913 63.636 86.364 72.727 77.273 cleeland-14 80.645 90.323 83.871 80.000 76.667 86.667 83.333 66.667 66.667 73.333 74.194 87.097 67.742 86.667 83.333 76.667 70.000 73.333 76.667 86.667 74.194 80.645 80.645 80.000 76.667 60.000 70.000 80.000 76.667 83.333 77.419 67.742 83.871 86.667 73.333 73.333 73.333 83.333 83.333 73.333 70.968 80.645 64.516 83.333 83.333 80.000 83.333 83.333 70.000 80.000 70.968 74.194 70.968 76.667 76.667 73.333 60.000 70.000 80.000 86.667 80.645 70.968 67.742 70.000 80.000 76.667 70.000 80.000 76.667 80.000 77.419 77.419 80.645 83.333 86.667 66.667 70.000 80.000 73.333 83.333 70.968 87.097 80.645 76.667 83.333 73.333 70.000 83.333 76.667 80.000 80.645 80.645 77.419 80.000 86.667 73.333 76.667 73.333 73.333 76.667 cmc 50.676 53.378 54.054 54.422 44.218 55.102 50.340 52.381 48.980 43.537 52.027 49.324 49.324 52.381 49.660 55.782 48.299 52.381 48.980 44.898 45.946 47.297 55.405 48.980 45.578 48.980 41.497 52.381 54.422 59.184 52.027 49.324 54.730 52.381 48.299 49.660 51.020 42.177 40.136 50.340 51.351 47.297 41.892 46.259 52.381 49.660 51.701 45.578 53.061 48.980 55.405 49.324 51.351 50.340 53.061 56.463 50.340 46.259 46.939 52.381 48.649 45.946 45.270 44.898 51.020 44.218 48.980 51.701 48.980 48.980 47.297 52.703 39.189 48.299 57.823 57.823 46.259 51.020 48.299 46.939 49.324 43.919 54.054 45.578 49.660 43.537 51.020 46.939 55.782 48.299 47.973 44.595 45.270 47.619 46.939 53.741 55.782 41.497 51.701 52.381 contact-lenses 100.000 100.000 66.667 100.000 50.000 100.000 100.000 100.000 0.000 100.000 66.667 100.000 66.667 100.000 100.000 100.000 50.000 100.000 100.000 100.000 66.667 100.000 100.000 100.000 50.000 100.000 100.000 50.000 100.000 50.000 66.667 66.667 100.000 100.000 100.000 100.000 100.000 50.000 100.000 50.000 100.000 66.667 100.000 66.667 100.000 100.000 100.000 100.000 50.000 50.000 66.667 100.000 66.667 66.667 50.000 100.000 100.000 100.000 100.000 100.000 66.667 100.000 100.000 100.000 100.000 50.000 100.000 50.000 100.000 50.000 100.000 100.000 100.000 66.667 100.000 100.000 50.000 100.000 50.000 50.000 66.667 100.000 66.667 100.000 100.000 50.000 100.000 50.000 100.000 100.000 66.667 66.667 100.000 66.667 100.000 50.000 100.000 100.000 100.000 100.000 credit 81.159 84.058 86.957 89.855 88.406 85.507 91.304 91.304 88.406 76.812 88.406 86.957 84.058 86.957 84.058 86.957 81.159 82.609 88.406 86.957 86.957 88.406 82.609 85.507 86.957 85.507 82.609 85.507 84.058 85.507 88.406 89.855 85.507 88.406 84.058 82.609 88.406 86.957 89.855 91.304 78.261 84.058 86.957 89.855 81.159 85.507 89.855 88.406 86.957 89.855 88.406 86.957 79.710 89.855 88.406 84.058 82.609 92.754 88.406 89.855 88.406 86.957 85.507 84.058 89.855 81.159 88.406 85.507 91.304 88.406 82.609 86.957 85.507 94.203 81.159 84.058 88.406 81.159 88.406 82.609 89.855 89.855 86.957 79.710 85.507 84.058 89.855 91.304 79.710 84.058 86.957 92.754 89.855 89.855 86.957 82.609 81.159 89.855 81.159 81.159 credit 81.159 84.058 86.957 89.855 88.406 85.507 91.304 91.304 88.406 76.812 88.406 86.957 84.058 86.957 84.058 86.957 81.159 82.609 88.406 86.957 86.957 88.406 82.609 85.507 86.957 85.507 82.609 85.507 84.058 85.507 88.406 89.855 85.507 88.406 84.058 82.609 88.406 86.957 89.855 91.304 78.261 84.058 86.957 89.855 81.159 85.507 89.855 88.406 86.957 89.855 88.406 86.957 79.710 89.855 88.406 84.058 82.609 92.754 88.406 89.855 88.406 86.957 85.507 84.058 89.855 81.159 88.406 85.507 91.304 88.406 82.609 86.957 85.507 94.203 81.159 84.058 88.406 81.159 88.406 82.609 89.855 89.855 86.957 79.710 85.507 84.058 89.855 91.304 79.710 84.058 86.957 92.754 89.855 89.855 86.957 82.609 81.159 89.855 81.159 81.159 ecoli 79.412 79.412 82.353 88.235 85.294 82.353 81.818 75.758 78.788 81.818 76.471 79.412 91.176 91.176 67.647 79.412 81.818 72.727 81.818 75.758 82.353 82.353 70.588 82.353 79.412 82.353 84.848 75.758 81.818 78.788 79.412 73.529 76.471 79.412 82.353 79.412 78.788 75.758 78.788 87.879 76.471 79.412 76.471 88.235 85.294 76.471 90.909 84.848 81.818 75.758 85.294 82.353 73.529 76.471 82.353 79.412 81.818 84.848 75.758 84.848 76.471 67.647 79.412 82.353 70.588 88.235 81.818 78.788 81.818 84.848 76.471 88.235 82.353 70.588 79.412 82.353 84.848 78.788 81.818 81.818 76.471 67.647 76.471 82.353 76.471 88.235 78.788 87.879 78.788 81.818 85.294 73.529 79.412 88.235 85.294 82.353 72.727 75.758 81.818 75.758 eucalyptus 58.108 58.108 56.757 67.568 60.811 71.622 73.973 67.123 69.863 58.904 60.811 62.162 70.270 56.757 67.568 63.514 61.644 63.014 68.493 71.233 64.865 59.459 67.568 66.216 70.270 64.865 50.685 67.123 64.384 64.384 63.514 72.973 67.568 55.405 54.054 60.811 65.753 61.644 64.384 65.753 68.919 70.270 63.514 66.216 60.811 62.162 64.384 54.795 64.384 63.014 64.865 66.216 64.865 62.162 68.919 63.514 58.904 67.123 65.753 61.644 68.919 64.865 68.919 63.514 60.811 63.514 63.014 52.055 72.603 69.863 60.811 64.865 68.919 63.514 66.216 62.162 65.753 52.055 58.904 65.753 68.919 63.514 72.973 77.027 64.865 71.622 71.233 54.795 54.795 45.205 60.811 64.865 70.270 64.865 70.270 60.811 71.233 64.384 65.753 63.014 german-credit 76.000 70.000 75.000 73.000 66.000 64.000 77.000 68.000 78.000 77.000 75.000 72.000 73.000 72.000 68.000 71.000 74.000 69.000 67.000 75.000 69.000 70.000 78.000 77.000 75.000 71.000 66.000 71.000 73.000 72.000 72.000 75.000 73.000 72.000 69.000 74.000 66.000 76.000 72.000 76.000 74.000 68.000 68.000 71.000 68.000 68.000 68.000 70.000 80.000 73.000 65.000 77.000 76.000 72.000 60.000 73.000 72.000 76.000 76.000 66.000 75.000 64.000 72.000 71.000 78.000 76.000 73.000 72.000 74.000 74.000 71.000 71.000 77.000 65.000 72.000 72.000 72.000 69.000 73.000 68.000 72.000 78.000 77.000 73.000 73.000 77.000 76.000 76.000 73.000 66.000 74.000 75.000 77.000 70.000 74.000 69.000 70.000 75.000 72.000 73.000 glass 54.545 72.727 77.273 63.636 76.190 85.714 66.667 80.952 66.667 71.429 68.182 86.364 81.818 63.636 66.667 66.667 90.476 61.905 61.905 76.190 72.727 86.364 77.273 63.636 61.905 76.190 57.143 85.714 71.429 66.667 59.091 81.818 72.727 59.091 66.667 76.190 52.381 90.476 71.429 76.190 72.727 77.273 77.273 77.273 80.952 76.190 66.667 80.952 80.952 66.667 86.364 77.273 77.273 77.273 76.190 76.190 52.381 61.905 80.952 61.905 68.182 63.636 77.273 72.727 90.476 90.476 80.952 61.905 66.667 76.190 72.727 72.727 86.364 63.636 61.905 42.857 80.952 76.190 61.905 76.190 72.727 68.182 81.818 77.273 71.429 85.714 71.429 66.667 61.905 76.190 90.909 77.273 72.727 63.636 90.476 66.667 71.429 38.095 66.667 66.667 grub-damage 43.750 43.750 43.750 31.250 18.750 46.667 33.333 33.333 53.333 40.000 37.500 37.500 43.750 31.250 31.250 40.000 33.333 40.000 46.667 13.333 37.500 37.500 50.000 50.000 31.250 53.333 33.333 33.333 40.000 46.667 31.250 43.750 43.750 37.500 37.500 40.000 46.667 40.000 40.000 33.333 56.250 31.250 37.500 43.750 37.500 26.667 33.333 40.000 33.333 40.000 18.750 37.500 50.000 43.750 50.000 20.000 20.000 60.000 40.000 46.667 31.250 37.500 43.750 31.250 56.250 40.000 40.000 40.000 40.000 60.000 18.750 50.000 31.250 62.500 43.750 53.333 33.333 26.667 46.667 40.000 50.000 50.000 56.250 37.500 25.000 46.667 53.333 20.000 53.333 26.667 31.250 43.750 50.000 31.250 56.250 33.333 46.667 40.000 33.333 40.000 haberman 70.968 74.194 70.968 74.194 74.194 74.194 73.333 70.000 73.333 73.333 67.742 74.194 64.516 74.194 74.194 70.968 73.333 73.333 76.667 73.333 74.194 70.968 74.194 74.194 70.968 58.065 73.333 73.333 73.333 73.333 74.194 67.742 74.194 70.968 70.968 70.968 70.000 73.333 73.333 56.667 74.194 74.194 74.194 61.290 74.194 70.968 73.333 73.333 73.333 70.000 74.194 74.194 70.968 64.516 74.194 70.968 73.333 73.333 66.667 73.333 67.742 74.194 74.194 74.194 74.194 67.742 73.333 70.000 60.000 73.333 74.194 74.194 67.742 61.290 74.194 67.742 66.667 73.333 73.333 73.333 74.194 67.742 67.742 67.742 74.194 70.968 73.333 73.333 73.333 73.333 64.516 74.194 70.968 70.968 70.968 70.968 73.333 73.333 73.333 73.333 hayes-roth 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 hepatitis 87.500 68.750 75.000 68.750 81.250 86.667 86.667 73.333 80.000 86.667 75.000 81.250 87.500 68.750 68.750 80.000 80.000 66.667 86.667 86.667 62.500 75.000 81.250 75.000 81.250 86.667 86.667 80.000 73.333 80.000 62.500 81.250 87.500 87.500 50.000 86.667 86.667 80.000 86.667 86.667 93.750 81.250 75.000 93.750 68.750 80.000 80.000 80.000 60.000 73.333 81.250 87.500 68.750 75.000 62.500 80.000 86.667 73.333 93.333 80.000 87.500 87.500 81.250 68.750 87.500 73.333 66.667 80.000 80.000 80.000 81.250 81.250 62.500 81.250 75.000 66.667 73.333 86.667 86.667 80.000 62.500 87.500 81.250 81.250 75.000 80.000 86.667 80.000 80.000 80.000 75.000 81.250 68.750 75.000 68.750 86.667 73.333 80.000 66.667 93.333 hungarian-14 76.667 76.667 76.667 63.333 82.759 86.207 75.862 96.552 72.414 79.310 76.667 83.333 83.333 83.333 82.759 79.310 72.414 79.310 93.103 75.862 76.667 83.333 73.333 80.000 93.103 79.310 82.759 93.103 55.172 72.414 83.333 76.667 76.667 80.000 72.414 86.207 68.966 86.207 89.655 86.207 83.333 86.667 73.333 76.667 86.207 89.655 75.862 79.310 79.310 86.207 76.667 83.333 80.000 90.000 79.310 89.655 75.862 68.966 82.759 82.759 80.000 80.000 80.000 86.667 75.862 82.759 79.310 93.103 68.966 89.655 76.667 76.667 93.333 90.000 82.759 75.862 89.655 75.862 72.414 79.310 76.667 83.333 76.667 83.333 72.414 79.310 75.862 82.759 89.655 82.759 73.333 76.667 76.667 90.000 82.759 82.759 86.207 75.862 72.414 86.207 hypothyroid 99.206 99.206 99.469 99.469 99.469 98.939 98.939 99.204 99.204 99.735 98.942 98.942 98.143 99.469 99.735 99.469 99.735 98.408 99.469 98.674 98.942 99.206 99.469 98.939 98.939 98.939 99.469 99.204 99.735 99.735 98.677 98.677 100.000 98.674 98.939 99.469 99.469 98.674 99.469 99.735 99.471 98.942 98.674 99.469 98.408 99.469 99.735 99.469 99.735 99.204 97.619 99.206 99.735 100.000 99.735 99.469 99.735 98.939 97.878 99.204 99.471 99.206 98.939 98.674 99.735 99.469 100.000 98.143 98.143 99.204 98.413 99.471 99.469 100.000 98.674 99.204 98.939 100.000 98.408 98.939 98.677 99.471 98.939 98.674 100.000 99.735 99.469 99.469 98.674 99.469 99.735 98.148 99.204 98.939 98.939 98.939 100.000 98.408 99.204 99.735 ionosphere 88.889 88.571 85.714 94.286 91.429 88.571 94.286 94.286 97.143 88.571 88.889 85.714 88.571 94.286 100.000 91.429 97.143 88.571 80.000 82.857 94.444 94.286 88.571 91.429 100.000 82.857 97.143 91.429 88.571 85.714 88.889 88.571 94.286 88.571 85.714 88.571 91.429 94.286 85.714 85.714 83.333 97.143 88.571 94.286 91.429 91.429 91.429 85.714 85.714 97.143 88.889 77.143 94.286 82.857 85.714 94.286 85.714 88.571 94.286 80.000 88.889 82.857 97.143 94.286 85.714 97.143 94.286 82.857 97.143 97.143 88.889 85.714 82.857 94.286 91.429 88.571 88.571 85.714 85.714 97.143 86.111 91.429 94.286 94.286 85.714 91.429 88.571 88.571 94.286 97.143 97.222 97.143 85.714 91.429 91.429 85.714 85.714 88.571 85.714 91.429 iris 93.333 100.000 100.000 100.000 86.667 93.333 86.667 86.667 93.333 93.333 93.333 80.000 86.667 100.000 100.000 93.333 100.000 93.333 86.667 100.000 100.000 86.667 93.333 93.333 86.667 100.000 86.667 86.667 93.333 93.333 86.667 93.333 80.000 86.667 100.000 93.333 100.000 100.000 93.333 100.000 100.000 80.000 93.333 100.000 93.333 100.000 93.333 93.333 93.333 93.333 93.333 100.000 93.333 86.667 93.333 93.333 93.333 93.333 100.000 100.000 93.333 86.667 86.667 93.333 93.333 100.000 93.333 93.333 93.333 100.000 86.667 93.333 93.333 100.000 100.000 86.667 93.333 100.000 93.333 86.667 93.333 100.000 86.667 93.333 93.333 100.000 93.333 93.333 93.333 100.000 100.000 100.000 100.000 86.667 93.333 93.333 93.333 86.667 93.333 93.333 kr-s-kp 98.750 99.375 99.062 98.750 100.000 99.688 99.373 100.000 100.000 99.373 99.375 99.062 99.375 99.688 99.688 99.375 99.373 99.373 99.687 99.687 99.688 99.062 99.688 99.688 99.375 98.438 99.687 99.687 99.373 99.373 99.375 99.062 98.438 99.375 99.375 100.000 100.000 99.687 99.373 99.687 99.375 100.000 99.375 99.375 99.062 99.375 99.373 100.000 99.060 99.687 99.375 99.062 99.688 99.688 99.062 99.375 99.373 100.000 99.373 99.373 98.438 99.375 99.062 99.688 99.375 99.688 100.000 100.000 100.000 99.060 99.688 99.375 99.062 99.688 99.375 98.750 99.373 100.000 99.687 99.373 99.688 99.688 99.062 99.062 99.375 99.375 99.373 99.373 99.687 98.746 99.062 99.062 99.688 99.688 100.000 100.000 99.373 99.060 99.060 99.687 labor 66.667 50.000 100.000 50.000 100.000 100.000 83.333 100.000 100.000 100.000 83.333 66.667 100.000 83.333 83.333 100.000 100.000 100.000 80.000 80.000 100.000 83.333 66.667 100.000 100.000 100.000 83.333 80.000 80.000 80.000 100.000 83.333 100.000 66.667 83.333 83.333 66.667 100.000 80.000 60.000 50.000 100.000 83.333 100.000 100.000 50.000 83.333 100.000 100.000 100.000 83.333 100.000 66.667 100.000 83.333 83.333 83.333 100.000 100.000 40.000 83.333 100.000 83.333 50.000 100.000 100.000 66.667 100.000 100.000 80.000 83.333 100.000 100.000 100.000 66.667 100.000 66.667 100.000 40.000 80.000 66.667 83.333 100.000 83.333 100.000 100.000 100.000 80.000 80.000 80.000 83.333 100.000 100.000 83.333 83.333 66.667 100.000 80.000 80.000 100.000 lier-disorders 48.571 57.143 54.286 57.143 57.143 58.824 58.824 55.882 52.941 61.765 57.143 57.143 57.143 57.143 57.143 55.882 55.882 58.824 50.000 58.824 51.429 51.429 62.857 54.286 57.143 58.824 58.824 58.824 55.882 58.824 54.286 62.857 57.143 57.143 62.857 58.824 55.882 58.824 58.824 52.941 51.429 57.143 57.143 60.000 57.143 55.882 58.824 61.765 58.824 50.000 51.429 60.000 42.857 57.143 57.143 58.824 58.824 58.824 50.000 58.824 60.000 57.143 54.286 57.143 57.143 38.235 58.824 55.882 58.824 58.824 54.286 60.000 57.143 57.143 57.143 58.824 61.765 61.765 52.941 58.824 45.714 57.143 51.429 60.000 57.143 58.824 58.824 58.824 61.765 58.824 62.857 62.857 57.143 62.857 48.571 58.824 58.824 50.000 61.765 58.824 lymphography 80.000 60.000 100.000 80.000 86.667 73.333 73.333 80.000 71.429 78.571 93.333 100.000 66.667 73.333 80.000 73.333 73.333 60.000 71.429 85.714 93.333 80.000 93.333 66.667 80.000 73.333 60.000 80.000 71.429 85.714 80.000 60.000 66.667 66.667 80.000 80.000 73.333 73.333 78.571 78.571 73.333 80.000 80.000 93.333 73.333 66.667 73.333 73.333 71.429 85.714 80.000 73.333 73.333 73.333 73.333 73.333 53.333 66.667 78.571 78.571 80.000 73.333 80.000 73.333 86.667 80.000 60.000 66.667 64.286 85.714 86.667 86.667 80.000 80.000 80.000 86.667 80.000 73.333 85.714 71.429 60.000 66.667 66.667 93.333 80.000 80.000 66.667 93.333 85.714 64.286 60.000 93.333 73.333 73.333 60.000 66.667 73.333 80.000 100.000 64.286 monks 65.574 65.000 55.000 65.000 65.000 65.000 66.667 66.667 66.667 66.667 52.459 68.333 66.667 55.000 66.667 65.000 65.000 55.000 65.000 65.000 65.574 56.667 60.000 66.667 66.667 65.000 65.000 65.000 65.000 66.667 62.295 65.000 61.667 65.000 60.000 65.000 73.333 66.667 66.667 66.667 65.574 65.000 65.000 56.667 61.667 65.000 66.667 66.667 58.333 63.333 65.574 58.333 66.667 56.667 66.667 65.000 66.667 65.000 65.000 65.000 65.574 65.000 65.000 65.000 65.000 51.667 66.667 60.000 66.667 66.667 65.574 66.667 66.667 56.667 66.667 65.000 58.333 55.000 65.000 66.667 65.574 65.000 65.000 65.000 65.000 66.667 60.000 56.667 66.667 66.667 65.574 66.667 66.667 66.667 66.667 65.000 65.000 65.000 65.000 65.000 monks1 100.000 100.000 96.429 98.214 98.214 100.000 100.000 96.364 98.182 100.000 100.000 100.000 100.000 92.857 100.000 100.000 98.182 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 91.071 100.000 100.000 92.727 100.000 100.000 91.071 100.000 92.857 100.000 100.000 85.455 100.000 90.909 100.000 100.000 100.000 100.000 100.000 96.429 94.643 94.545 100.000 100.000 94.545 96.429 98.214 98.214 100.000 89.286 100.000 100.000 96.364 100.000 98.182 98.214 89.286 100.000 100.000 98.214 100.000 100.000 90.909 87.273 100.000 100.000 96.429 100.000 94.643 100.000 100.000 100.000 90.909 92.727 96.364 100.000 96.429 100.000 100.000 91.071 100.000 100.000 100.000 100.000 96.364 94.643 100.000 98.214 100.000 100.000 96.429 100.000 100.000 100.000 92.727 monks3 100.000 100.000 96.429 100.000 100.000 100.000 100.000 96.364 100.000 96.364 96.429 96.429 100.000 100.000 98.182 100.000 100.000 98.182 100.000 100.000 100.000 98.214 100.000 96.429 98.182 100.000 98.182 100.000 98.182 100.000 100.000 98.214 100.000 100.000 98.182 100.000 100.000 100.000 96.364 96.364 98.214 100.000 100.000 98.214 96.364 98.182 100.000 98.182 100.000 100.000 100.000 98.214 96.429 100.000 100.000 100.000 100.000 96.364 100.000 98.182 98.214 100.000 100.000 98.214 98.182 100.000 96.364 100.000 100.000 98.182 96.429 100.000 100.000 98.214 100.000 100.000 98.182 98.182 98.182 100.000 98.214 100.000 100.000 96.429 98.182 98.182 98.182 100.000 100.000 100.000 98.214 100.000 98.214 100.000 98.182 100.000 100.000 98.182 98.182 98.182 mushroom 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 nursery 97.685 96.991 97.222 96.682 97.685 96.142 96.836 97.377 96.991 96.914 97.299 97.917 97.222 97.145 96.528 97.994 96.605 96.914 97.299 96.836 97.762 97.531 97.608 96.219 97.299 97.840 97.145 96.914 96.759 96.914 97.068 97.917 96.605 97.917 97.454 96.682 97.454 97.222 96.451 97.685 96.759 97.299 97.531 97.145 96.528 98.225 97.377 97.068 96.836 96.682 96.991 97.299 97.840 97.299 97.377 96.991 97.299 97.068 96.682 96.759 97.762 97.377 97.377 97.377 97.608 97.377 96.296 97.762 96.528 96.991 96.296 96.451 97.377 97.531 97.068 98.071 97.299 97.454 96.914 96.836 97.222 97.068 97.840 96.991 97.917 96.914 97.068 97.145 96.991 97.145 96.914 97.608 97.531 96.914 97.222 96.682 97.762 97.762 97.531 96.373 optdigits 79.537 79.537 74.911 79.537 82.562 81.317 78.826 79.537 76.868 77.046 76.335 81.139 83.274 76.868 75.979 78.826 81.139 78.114 78.826 77.402 77.580 78.470 79.893 79.181 77.936 74.199 79.004 76.335 77.046 80.427 80.249 77.046 80.605 77.580 78.114 81.317 77.224 78.826 75.623 78.114 75.623 76.868 78.648 77.936 78.292 80.427 79.715 80.783 75.267 78.114 77.758 75.801 76.868 80.249 81.851 77.580 80.783 80.249 74.377 78.114 79.004 81.139 77.936 76.690 77.402 77.758 76.335 81.851 79.181 79.893 76.335 78.292 80.605 79.004 77.580 80.961 79.537 79.893 78.470 78.114 79.359 76.512 79.715 74.021 80.427 78.292 76.690 74.021 80.783 78.648 76.512 79.715 79.893 79.715 82.740 80.783 80.605 78.826 81.317 77.046 owel 96.168 95.803 96.168 97.623 96.892 96.344 96.709 96.527 97.989 95.978 96.533 96.168 96.533 95.612 97.258 96.527 97.989 96.709 96.709 94.881 96.715 97.080 96.715 95.612 97.258 96.161 95.795 96.892 97.441 96.527 97.445 96.898 97.080 95.612 97.258 97.075 96.344 97.075 96.161 94.881 97.628 96.533 95.438 97.623 95.978 96.527 96.892 97.075 95.978 96.892 97.263 96.898 96.533 96.344 95.430 95.247 96.527 97.806 96.527 97.623 96.533 97.263 96.715 97.441 95.064 96.892 96.527 96.527 97.623 96.709 96.533 96.715 96.533 95.978 97.075 97.075 96.892 96.344 96.161 95.978 96.533 95.073 97.445 95.978 96.161 96.709 95.795 97.441 96.709 97.441 96.898 97.445 96.715 96.892 97.623 96.892 95.247 96.892 96.527 95.612 page-blocks 100.000 75.000 75.000 75.000 50.000 75.000 66.667 66.667 100.000 66.667 50.000 100.000 75.000 75.000 75.000 50.000 33.333 66.667 66.667 66.667 75.000 50.000 50.000 75.000 100.000 100.000 66.667 66.667 100.000 33.333 100.000 75.000 75.000 75.000 75.000 75.000 66.667 66.667 66.667 100.000 75.000 75.000 75.000 75.000 75.000 75.000 66.667 100.000 66.667 66.667 75.000 100.000 100.000 50.000 100.000 100.000 66.667 100.000 66.667 66.667 100.000 75.000 75.000 75.000 75.000 75.000 33.333 66.667 33.333 66.667 75.000 50.000 75.000 75.000 100.000 25.000 66.667 66.667 66.667 66.667 75.000 100.000 50.000 75.000 75.000 100.000 100.000 66.667 100.000 33.333 50.000 50.000 100.000 50.000 75.000 100.000 100.000 66.667 66.667 66.667 pasture-production 88.000 89.273 90.264 87.443 88.717 87.989 89.263 89.081 91.265 89.172 88.000 89.818 89.354 87.625 89.900 89.536 89.081 88.990 87.898 86.533 88.455 90.000 88.080 87.079 88.990 87.079 88.535 89.081 88.990 86.806 87.545 88.091 88.535 89.900 87.625 87.807 90.173 89.627 87.534 90.082 89.273 88.909 89.900 89.536 88.171 88.353 90.264 87.170 87.352 88.353 88.364 89.273 87.625 89.900 90.355 86.442 87.989 88.535 88.444 88.990 88.091 88.545 89.081 87.716 89.900 88.262 87.625 88.444 89.081 88.535 88.000 88.727 87.807 89.627 88.353 89.536 86.715 89.809 88.626 88.353 88.091 88.273 90.173 88.990 89.536 86.988 88.808 89.081 88.262 88.990 88.636 89.182 88.717 86.442 89.172 86.624 89.900 88.353 87.443 88.717 pendigits 76.623 72.727 75.325 77.922 74.026 74.026 70.130 70.130 69.737 76.316 68.831 63.636 76.623 74.026 77.922 80.519 72.727 68.831 76.316 75.000 72.727 71.429 75.325 74.026 83.117 67.532 72.727 75.325 81.579 81.579 81.818 79.221 71.429 70.130 70.130 76.623 75.325 80.519 72.368 72.368 81.818 66.234 77.922 79.221 77.922 79.221 72.727 72.727 72.368 77.632 75.325 75.325 68.831 76.623 68.831 76.623 75.325 83.117 72.368 67.105 66.234 76.623 76.623 64.935 77.922 66.234 80.519 75.325 78.947 82.895 81.818 76.623 74.026 71.429 67.532 79.221 71.429 67.532 71.053 76.316 70.130 75.325 72.727 70.130 63.636 71.429 76.623 83.117 78.947 75.000 70.130 75.325 77.922 74.026 79.221 72.727 77.922 77.922 71.053 61.842 pima-diabetes 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 66.667 66.667 77.778 77.778 77.778 77.778 66.667 55.556 66.667 66.667 66.667 66.667 77.778 77.778 77.778 77.778 66.667 66.667 66.667 66.667 66.667 66.667 77.778 77.778 66.667 77.778 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 55.556 66.667 66.667 77.778 66.667 77.778 77.778 66.667 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 77.778 44.444 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 55.556 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 44.444 77.778 77.778 77.778 77.778 66.667 66.667 66.667 66.667 77.778 77.778 77.778 77.778 66.667 66.667 postoperatie 38.235 41.176 38.235 50.000 38.235 38.235 41.176 44.118 35.294 36.364 35.294 38.235 38.235 35.294 41.176 44.118 44.118 32.353 50.000 42.424 44.118 41.176 35.294 44.118 38.235 44.118 35.294 35.294 38.235 54.545 35.294 47.059 35.294 38.235 41.176 41.176 41.176 44.118 38.235 36.364 32.353 47.059 52.941 52.941 29.412 47.059 41.176 44.118 41.176 30.303 44.118 35.294 47.059 35.294 38.235 38.235 23.529 41.176 35.294 54.545 26.471 35.294 41.176 47.059 44.118 35.294 50.000 29.412 47.059 48.485 52.941 41.176 55.882 50.000 38.235 44.118 35.294 47.059 38.235 30.303 35.294 35.294 44.118 38.235 44.118 41.176 32.353 47.059 44.118 57.576 47.059 47.059 41.176 35.294 41.176 41.176 38.235 52.941 41.176 36.364 primary-tumor 93.939 94.805 93.074 94.372 93.939 94.805 93.074 92.641 93.939 97.835 93.939 91.342 96.537 93.074 94.805 95.238 92.208 94.372 96.537 94.805 94.805 96.970 95.671 93.506 93.074 96.104 94.372 91.775 92.641 92.208 94.805 96.104 94.372 94.805 93.939 94.372 94.805 93.074 95.238 92.208 91.342 94.805 92.641 96.104 92.208 91.342 91.342 94.805 93.506 95.238 96.104 91.342 95.238 93.939 95.238 90.476 97.403 94.805 92.641 95.671 96.104 93.074 93.074 93.074 95.238 92.641 94.372 96.104 96.104 92.641 93.506 94.372 92.208 91.775 96.104 93.939 94.805 93.939 94.372 94.372 94.805 92.641 92.208 92.208 92.208 93.074 95.238 96.970 94.805 97.403 94.805 94.805 94.805 92.208 93.506 94.805 95.671 93.074 96.537 94.372 segment 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 90.909 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 90.909 87.879 90.625 90.625 90.625 87.500 87.500 87.500 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 87.500 87.500 87.500 90.625 90.625 90.625 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 solar-flare-C 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 78.125 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 solar-flare-X 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 solar-flare-m 85.714 80.952 80.952 76.190 66.667 66.667 71.429 76.190 65.000 75.000 71.429 66.667 80.952 80.952 80.952 71.429 90.476 71.429 85.000 100.000 71.429 66.667 95.238 80.952 80.952 76.190 61.905 61.905 70.000 75.000 71.429 80.952 71.429 61.905 76.190 66.667 57.143 71.429 80.000 90.000 76.190 76.190 85.714 76.190 71.429 71.429 71.429 80.952 85.000 80.000 80.952 61.905 76.190 61.905 76.190 57.143 95.238 90.476 95.000 60.000 80.952 80.952 80.952 76.190 76.190 85.714 71.429 71.429 70.000 75.000 76.190 76.190 71.429 85.714 57.143 71.429 80.952 85.714 65.000 75.000 80.952 76.190 66.667 80.952 76.190 76.190 52.381 76.190 85.000 60.000 76.190 85.714 71.429 47.619 61.905 57.143 71.429 80.952 55.000 75.000 sonar 92.754 92.754 89.855 89.706 95.588 89.706 95.588 94.118 91.176 92.647 88.406 94.203 89.855 95.588 91.176 91.176 91.176 89.706 86.765 97.059 94.203 92.754 91.304 91.176 94.118 92.647 92.647 92.647 97.059 88.235 94.203 95.652 94.203 91.176 88.235 91.176 94.118 97.059 95.588 89.706 92.754 92.754 91.304 91.176 97.059 89.706 89.706 97.059 89.706 92.647 91.304 97.101 94.203 94.118 91.176 95.588 92.647 88.235 92.647 92.647 91.304 91.304 94.203 94.118 92.647 89.706 91.176 97.059 85.294 98.529 92.754 94.203 97.101 92.647 86.765 92.647 89.706 91.176 94.118 97.059 89.855 92.754 92.754 89.706 91.176 92.647 92.647 100.000 94.118 97.059 92.754 92.754 94.203 92.647 91.176 92.647 91.176 92.647 91.176 95.588 soybean 94.143 91.522 92.609 95.000 92.609 91.957 93.478 92.174 91.522 93.043 93.059 93.043 93.913 91.087 92.826 91.522 94.783 93.261 91.957 92.609 92.842 94.783 93.696 93.261 92.391 93.913 94.565 91.304 93.913 93.043 91.323 93.478 93.696 90.870 93.043 94.130 92.609 93.261 93.696 90.870 92.625 92.391 93.913 92.826 91.522 93.696 92.826 94.783 91.304 92.826 91.757 93.478 93.261 92.826 94.565 92.391 92.826 92.391 91.739 95.000 92.842 91.957 93.696 95.652 92.174 90.435 92.174 90.870 95.000 92.609 91.974 93.261 93.913 92.391 95.000 91.957 92.174 91.522 93.261 92.609 93.709 91.087 94.130 95.000 91.739 93.696 91.957 90.217 91.522 92.826 92.408 94.130 93.478 94.130 92.174 92.174 91.739 91.739 93.478 92.609 spambase 85.185 62.963 85.185 77.778 77.778 70.370 85.185 73.077 84.615 80.769 77.778 85.185 85.185 77.778 81.481 88.889 85.185 84.615 92.308 76.923 85.185 85.185 85.185 77.778 88.889 88.889 74.074 92.308 76.923 80.769 81.481 74.074 81.481 70.370 81.481 77.778 88.889 88.462 80.769 92.308 70.370 85.185 77.778 81.481 81.481 81.481 77.778 84.615 88.462 80.769 85.185 74.074 88.889 81.481 77.778 81.481 81.481 80.769 69.231 80.769 70.370 85.185 81.481 81.481 85.185 74.074 77.778 80.769 73.077 88.462 74.074 96.296 74.074 92.593 70.370 85.185 77.778 76.923 73.077 84.615 85.185 85.185 85.185 77.778 62.963 81.481 74.074 84.615 65.385 88.462 85.185 85.185 81.481 85.185 92.593 66.667 77.778 88.462 84.615 84.615 spect-reordered 95.925 94.671 93.417 94.671 94.984 96.552 91.536 92.163 95.298 94.357 93.417 94.671 94.984 91.223 94.671 93.417 94.357 95.925 94.984 94.984 93.730 94.044 93.730 93.103 94.357 94.044 94.984 95.611 94.044 91.536 93.730 93.730 94.044 94.984 95.925 94.671 93.730 93.103 96.238 92.163 94.044 95.925 95.298 93.417 93.103 94.357 95.298 94.984 91.850 91.223 94.044 91.223 93.730 92.476 95.298 97.179 94.984 95.298 94.671 94.671 94.984 93.417 92.790 93.103 93.103 94.671 94.984 94.984 95.298 96.238 92.476 94.357 91.536 94.357 93.103 92.790 92.476 95.611 94.984 94.357 94.044 94.671 93.730 92.163 95.925 94.357 93.730 94.357 94.357 95.298 95.298 94.044 95.611 94.671 95.611 92.790 93.417 92.790 94.357 94.984 splice 50.000 50.000 80.000 80.000 80.000 60.000 60.000 80.000 100.000 60.000 66.667 50.000 80.000 80.000 60.000 100.000 100.000 60.000 20.000 60.000 100.000 66.667 60.000 60.000 20.000 60.000 60.000 60.000 80.000 60.000 83.333 83.333 60.000 60.000 20.000 80.000 40.000 60.000 60.000 60.000 83.333 66.667 60.000 80.000 40.000 80.000 80.000 40.000 20.000 80.000 66.667 33.333 40.000 80.000 80.000 40.000 60.000 40.000 80.000 60.000 66.667 83.333 80.000 40.000 80.000 60.000 80.000 60.000 60.000 60.000 50.000 66.667 40.000 60.000 80.000 80.000 80.000 60.000 60.000 60.000 33.333 50.000 60.000 80.000 80.000 80.000 40.000 80.000 60.000 20.000 66.667 33.333 60.000 40.000 60.000 60.000 80.000 80.000 80.000 80.000 squash-stored 66.667 100.000 100.000 100.000 60.000 80.000 20.000 80.000 60.000 100.000 50.000 83.333 100.000 60.000 100.000 80.000 80.000 100.000 40.000 80.000 83.333 83.333 100.000 100.000 40.000 60.000 60.000 80.000 100.000 60.000 83.333 66.667 80.000 40.000 100.000 80.000 100.000 80.000 80.000 100.000 50.000 83.333 80.000 100.000 100.000 80.000 80.000 80.000 60.000 60.000 83.333 66.667 80.000 60.000 100.000 60.000 80.000 60.000 80.000 100.000 66.667 50.000 100.000 80.000 100.000 80.000 100.000 40.000 60.000 80.000 66.667 16.667 100.000 80.000 100.000 80.000 100.000 100.000 80.000 60.000 66.667 66.667 100.000 80.000 40.000 60.000 100.000 80.000 80.000 80.000 50.000 83.333 80.000 100.000 100.000 100.000 60.000 80.000 80.000 80.000 squash-unstored 50.000 40.000 60.000 46.667 60.000 40.000 40.000 40.000 46.667 46.667 37.500 46.667 60.000 53.333 53.333 40.000 40.000 33.333 60.000 46.667 43.750 40.000 53.333 33.333 53.333 46.667 53.333 46.667 46.667 53.333 37.500 46.667 46.667 40.000 33.333 60.000 40.000 53.333 53.333 60.000 43.750 53.333 46.667 40.000 46.667 46.667 53.333 46.667 53.333 40.000 37.500 53.333 60.000 40.000 33.333 46.667 46.667 53.333 46.667 46.667 31.250 46.667 46.667 40.000 53.333 53.333 53.333 60.000 46.667 40.000 50.000 26.667 46.667 40.000 53.333 53.333 40.000 53.333 40.000 60.000 50.000 46.667 60.000 46.667 46.667 46.667 40.000 40.000 53.333 40.000 43.750 46.667 46.667 46.667 40.000 33.333 46.667 53.333 46.667 53.333 tae 77.778 81.818 71.717 74.747 79.798 81.818 73.737 76.768 78.788 69.697 75.758 81.818 73.737 78.788 84.848 70.707 71.717 83.838 74.747 78.788 79.798 75.758 77.778 79.798 70.707 80.808 67.677 82.828 80.808 78.788 76.768 76.768 81.818 77.778 78.788 77.778 77.778 78.788 81.818 76.768 74.747 85.859 75.758 70.707 70.707 78.788 74.747 76.768 66.667 78.788 76.768 74.747 64.646 79.798 86.869 74.747 76.768 64.646 82.828 77.778 72.727 77.778 75.758 73.737 75.758 73.737 75.758 75.758 84.848 78.788 76.768 77.778 80.808 69.697 79.798 73.737 62.626 71.717 76.768 80.808 74.747 79.798 68.687 73.737 79.798 75.758 74.747 69.697 77.778 84.848 81.818 71.717 79.798 74.747 68.687 78.788 74.747 81.818 74.747 77.778 waveform 75.000 74.000 75.400 71.800 73.000 75.000 76.200 75.000 73.600 74.800 75.000 76.400 76.000 73.400 74.800 77.800 76.200 76.200 72.000 74.200 70.400 72.800 73.400 78.000 73.800 73.400 75.000 75.400 74.000 76.400 74.600 73.600 74.600 78.400 73.400 71.200 76.800 72.800 77.000 76.600 76.600 74.400 74.400 74.600 76.800 75.600 77.200 75.200 76.800 71.400 78.400 73.200 74.400 74.200 75.400 72.400 71.200 77.000 75.200 74.000 76.000 74.800 77.800 73.200 80.200 72.800 76.000 75.200 73.200 75.400 80.200 75.200 75.200 76.400 76.000 76.000 75.000 75.200 74.400 73.200 75.400 74.800 77.400 74.600 75.200 71.000 74.600 73.800 72.800 70.800 73.400 73.000 73.400 75.400 74.400 75.400 77.400 73.200 78.000 76.000 white-clover 71.429 42.857 71.429 83.333 50.000 66.667 50.000 33.333 33.333 66.667 57.143 42.857 57.143 66.667 66.667 66.667 66.667 33.333 33.333 100.000 85.714 85.714 57.143 66.667 33.333 66.667 33.333 66.667 66.667 33.333 57.143 71.429 71.429 66.667 50.000 83.333 16.667 50.000 66.667 66.667 100.000 71.429 71.429 50.000 50.000 83.333 66.667 66.667 33.333 50.000 71.429 42.857 71.429 83.333 83.333 33.333 83.333 50.000 66.667 66.667 57.143 42.857 71.429 83.333 66.667 50.000 33.333 66.667 66.667 16.667 57.143 100.000 57.143 66.667 83.333 66.667 50.000 50.000 33.333 50.000 71.429 100.000 71.429 66.667 50.000 66.667 50.000 66.667 50.000 66.667 71.429 71.429 57.143 66.667 66.667 66.667 50.000 66.667 83.333 16.667 wine 88.889 88.889 88.889 77.778 94.444 83.333 94.444 88.889 94.118 88.235 94.444 83.333 88.889 94.444 100.000 94.444 94.444 100.000 76.471 82.353 100.000 88.889 88.889 83.333 88.889 94.444 88.889 100.000 94.118 88.235 88.889 100.000 94.444 88.889 88.889 83.333 83.333 83.333 100.000 88.235 88.889 94.444 88.889 83.333 94.444 100.000 88.889 94.444 88.235 100.000 77.778 88.889 100.000 88.889 88.889 88.889 100.000 94.444 94.118 88.235 83.333 100.000 83.333 100.000 88.889 94.444 83.333 88.889 88.235 94.118 83.333 94.444 88.889 88.889 83.333 100.000 94.444 94.444 94.118 88.235 100.000 94.444 88.889 94.444 94.444 83.333 100.000 88.889 94.118 94.118 94.444 83.333 94.444 83.333 88.889 94.444 83.333 83.333 88.235 88.235 wisconsin-breast-cancer 98.571 92.857 97.143 84.286 90.000 95.714 92.857 95.714 97.143 92.754 92.857 95.714 95.714 92.857 100.000 94.286 98.571 90.000 94.286 98.551 100.000 92.857 92.857 95.714 90.000 95.714 98.571 97.143 90.000 98.551 94.286 95.714 95.714 98.571 87.143 92.857 95.714 97.143 95.714 95.652 97.143 92.857 94.286 90.000 95.714 97.143 98.571 97.143 95.714 92.754 94.286 100.000 95.714 94.286 91.429 97.143 98.571 91.429 94.286 97.101 94.286 95.714 97.143 91.429 95.714 95.714 95.714 95.714 91.429 100.000 94.286 98.571 100.000 98.571 92.857 91.429 94.286 94.286 97.143 95.652 97.143 97.143 94.286 94.286 97.143 92.857 92.857 95.714 95.714 92.754 92.857 91.429 95.714 95.714 90.000 94.286 98.571 98.571 97.143 94.203 yeast 59.060 54.362 57.047 59.060 52.027 58.108 60.135 61.486 55.405 53.378 59.060 60.403 55.705 53.020 62.162 55.405 45.946 54.054 58.108 63.514 56.376 62.416 59.060 55.034 60.811 57.432 56.081 52.027 60.811 57.432 57.047 57.047 51.007 61.074 55.405 59.459 59.459 51.351 61.486 54.730 55.034 57.047 61.745 53.691 52.027 54.730 56.757 56.757 55.405 55.405 53.020 56.376 56.376 59.732 53.378 62.162 55.405 60.135 54.054 64.865 57.718 56.376 57.718 57.718 58.108 60.135 52.703 55.405 58.108 56.081 52.349 60.403 61.074 52.349 59.459 54.054 56.757 62.162 52.703 61.486 51.678 57.047 62.416 58.389 53.378 60.135 53.378 54.730 67.568 53.378 59.732 59.060 55.705 55.034 57.432 62.162 58.108 58.108 53.378 56.757 zoo 81.818 100.000 100.000 80.000 90.000 100.000 80.000 100.000 90.000 100.000 90.909 90.000 90.000 90.000 100.000 80.000 100.000 100.000 100.000 90.000 90.909 100.000 100.000 100.000 80.000 100.000 90.000 80.000 100.000 90.000 90.909 100.000 100.000 90.000 100.000 80.000 90.000 90.000 90.000 90.000 100.000 100.000 90.000 100.000 90.000 90.000 80.000 90.000 90.000 90.000 81.818 100.000 90.000 100.000 90.000 90.000 90.000 100.000 100.000 90.000 90.909 90.000 100.000 90.000 90.000 100.000 90.000 90.000 100.000 100.000 90.909 100.000 100.000 90.000 100.000 80.000 100.000 70.000 100.000 90.000 81.818 90.000 100.000 100.000 100.000 80.000 80.000 90.000 100.000 100.000 90.909 100.000 90.000 80.000 100.000 100.000 100.000 90.000 80.000 90.000 j48gr anneal 96.667 100.000 97.778 100.000 97.778 98.889 98.889 98.889 98.876 97.753 98.889 98.889 98.889 97.778 98.889 98.889 98.889 96.667 100.000 100.000 98.889 97.778 98.889 98.889 93.333 98.889 97.778 98.889 98.876 100.000 97.778 98.889 97.778 98.889 96.667 97.778 100.000 96.667 100.000 98.876 97.778 100.000 97.778 97.778 100.000 98.889 100.000 98.889 98.876 98.876 98.889 100.000 100.000 98.889 97.778 97.778 97.778 96.667 100.000 98.876 96.667 98.889 98.889 98.889 100.000 98.889 98.889 97.778 98.876 98.876 97.778 100.000 100.000 97.778 97.778 100.000 98.889 100.000 96.629 100.000 98.889 97.778 100.000 98.889 100.000 98.889 97.778 97.778 98.876 98.876 100.000 98.889 98.889 100.000 100.000 96.667 97.778 98.889 97.753 98.876 audiology 65.217 86.957 78.261 82.609 73.913 82.609 90.909 77.273 77.273 68.182 86.957 86.957 78.261 69.565 73.913 69.565 72.727 77.273 72.727 77.273 82.609 60.870 78.261 73.913 78.261 73.913 81.818 90.909 68.182 81.818 60.870 78.261 73.913 69.565 73.913 82.609 77.273 95.455 86.364 86.364 78.261 69.565 73.913 78.261 78.261 73.913 72.727 81.818 86.364 86.364 69.565 86.957 78.261 73.913 78.261 78.261 81.818 72.727 63.636 72.727 69.565 69.565 91.304 73.913 86.957 78.261 72.727 81.818 72.727 77.273 73.913 65.217 82.609 78.261 78.261 82.609 90.909 72.727 63.636 77.273 78.261 86.957 78.261 69.565 78.261 78.261 72.727 72.727 77.273 86.364 78.261 82.609 95.652 86.957 52.174 73.913 63.636 86.364 72.727 77.273 cleeland-14 80.645 90.323 83.871 80.000 76.667 86.667 83.333 66.667 66.667 70.000 74.194 87.097 67.742 86.667 83.333 76.667 70.000 73.333 80.000 86.667 74.194 80.645 80.645 80.000 76.667 60.000 70.000 80.000 76.667 83.333 77.419 67.742 83.871 86.667 73.333 73.333 73.333 80.000 83.333 73.333 70.968 77.419 64.516 83.333 83.333 83.333 86.667 83.333 70.000 80.000 70.968 74.194 70.968 76.667 76.667 73.333 60.000 70.000 80.000 86.667 80.645 70.968 67.742 70.000 80.000 76.667 70.000 80.000 76.667 80.000 77.419 77.419 80.645 83.333 86.667 66.667 70.000 80.000 73.333 80.000 70.968 87.097 80.645 76.667 83.333 73.333 73.333 83.333 76.667 76.667 87.097 80.645 77.419 80.000 86.667 73.333 76.667 73.333 73.333 76.667 cmc 50.676 53.378 54.054 54.422 44.218 55.102 51.020 52.381 48.980 43.537 52.027 49.324 49.324 53.061 49.660 55.782 48.299 52.381 48.980 44.898 45.946 47.297 55.405 48.980 45.578 48.980 41.497 52.381 54.422 59.184 52.027 49.324 54.730 52.381 48.299 49.660 51.020 42.857 40.136 50.340 51.351 47.297 41.892 46.259 52.381 49.660 51.701 45.578 53.061 48.980 55.405 50.000 51.351 50.340 53.061 56.463 50.340 46.259 46.939 53.061 48.649 45.946 45.270 44.898 51.020 44.218 48.980 51.701 48.980 48.980 47.297 52.703 39.189 48.980 57.823 57.823 46.259 51.020 48.299 46.939 49.324 43.919 54.054 45.578 48.980 44.218 51.020 46.939 55.782 48.299 47.973 44.595 45.270 46.939 46.939 53.741 55.782 41.497 51.701 52.381 contact-lenses 100.000 100.000 66.667 100.000 50.000 100.000 100.000 100.000 0.000 100.000 66.667 100.000 66.667 100.000 100.000 100.000 50.000 100.000 100.000 100.000 66.667 100.000 100.000 100.000 50.000 100.000 100.000 50.000 100.000 50.000 66.667 66.667 100.000 100.000 100.000 100.000 100.000 50.000 100.000 50.000 100.000 66.667 100.000 66.667 100.000 100.000 100.000 100.000 50.000 50.000 66.667 100.000 66.667 66.667 50.000 100.000 100.000 100.000 100.000 100.000 66.667 100.000 100.000 100.000 100.000 50.000 100.000 50.000 100.000 50.000 100.000 100.000 100.000 66.667 100.000 100.000 50.000 100.000 50.000 50.000 66.667 100.000 66.667 100.000 100.000 50.000 100.000 50.000 100.000 100.000 66.667 66.667 100.000 66.667 100.000 50.000 100.000 100.000 100.000 100.000 credit 82.609 84.058 86.957 89.855 88.406 85.507 91.304 91.304 88.406 76.812 88.406 86.957 85.507 86.957 84.058 86.957 81.159 82.609 88.406 86.957 86.957 88.406 82.609 85.507 86.957 85.507 82.609 85.507 84.058 85.507 88.406 89.855 85.507 88.406 84.058 82.609 88.406 86.957 91.304 91.304 78.261 84.058 86.957 89.855 81.159 86.957 89.855 88.406 86.957 89.855 88.406 86.957 79.710 89.855 88.406 84.058 84.058 92.754 88.406 89.855 88.406 86.957 85.507 84.058 89.855 81.159 88.406 85.507 92.754 88.406 84.058 86.957 85.507 94.203 81.159 84.058 88.406 81.159 88.406 82.609 89.855 89.855 86.957 79.710 85.507 84.058 91.304 91.304 79.710 84.058 86.957 92.754 89.855 89.855 86.957 82.609 81.159 89.855 81.159 81.159 credit 82.609 84.058 86.957 89.855 88.406 85.507 91.304 91.304 88.406 76.812 88.406 86.957 85.507 86.957 84.058 86.957 81.159 82.609 88.406 86.957 86.957 88.406 82.609 85.507 86.957 85.507 82.609 85.507 84.058 85.507 88.406 89.855 85.507 88.406 84.058 82.609 88.406 86.957 91.304 91.304 78.261 84.058 86.957 89.855 81.159 86.957 89.855 88.406 86.957 89.855 88.406 86.957 79.710 89.855 88.406 84.058 84.058 92.754 88.406 89.855 88.406 86.957 85.507 84.058 89.855 81.159 88.406 85.507 92.754 88.406 84.058 86.957 85.507 94.203 81.159 84.058 88.406 81.159 88.406 82.609 89.855 89.855 86.957 79.710 85.507 84.058 91.304 91.304 79.710 84.058 86.957 92.754 89.855 89.855 86.957 82.609 81.159 89.855 81.159 81.159 ecoli 79.412 79.412 82.353 88.235 85.294 82.353 81.818 75.758 78.788 81.818 76.471 82.353 91.176 91.176 70.588 79.412 81.818 72.727 81.818 75.758 82.353 82.353 73.529 85.294 79.412 82.353 84.848 75.758 81.818 78.788 79.412 73.529 76.471 79.412 82.353 79.412 78.788 81.818 75.758 87.879 76.471 79.412 76.471 88.235 85.294 76.471 90.909 84.848 81.818 75.758 85.294 82.353 73.529 76.471 82.353 79.412 81.818 84.848 78.788 84.848 76.471 67.647 79.412 82.353 70.588 88.235 81.818 78.788 81.818 84.848 76.471 88.235 82.353 70.588 79.412 85.294 84.848 78.788 81.818 81.818 76.471 67.647 76.471 82.353 76.471 88.235 78.788 87.879 78.788 81.818 85.294 73.529 79.412 88.235 85.294 82.353 72.727 75.758 81.818 75.758 eucalyptus 58.108 58.108 56.757 67.568 59.459 71.622 73.973 65.753 69.863 58.904 63.514 62.162 70.270 56.757 67.568 62.162 61.644 63.014 68.493 71.233 64.865 58.108 67.568 67.568 70.270 64.865 50.685 67.123 64.384 64.384 64.865 72.973 67.568 55.405 54.054 60.811 65.753 61.644 65.753 65.753 68.919 70.270 63.514 66.216 60.811 62.162 64.384 54.795 64.384 63.014 64.865 66.216 64.865 62.162 68.919 63.514 58.904 67.123 64.384 61.644 68.919 64.865 68.919 66.216 60.811 63.514 61.644 52.055 72.603 69.863 60.811 64.865 67.568 63.514 66.216 62.162 65.753 50.685 60.274 64.384 68.919 63.514 72.973 77.027 64.865 71.622 69.863 56.164 54.795 45.205 60.811 64.865 68.919 64.865 70.270 60.811 69.863 64.384 68.493 63.014 german-credit 77.000 68.000 76.000 73.000 66.000 64.000 77.000 68.000 78.000 77.000 75.000 72.000 73.000 72.000 68.000 70.000 74.000 69.000 68.000 76.000 68.000 70.000 78.000 77.000 75.000 71.000 66.000 71.000 73.000 73.000 72.000 75.000 73.000 72.000 69.000 74.000 67.000 76.000 72.000 76.000 74.000 68.000 68.000 71.000 68.000 68.000 69.000 70.000 80.000 73.000 65.000 77.000 76.000 73.000 60.000 72.000 72.000 75.000 76.000 66.000 75.000 64.000 73.000 70.000 78.000 76.000 73.000 72.000 74.000 74.000 71.000 71.000 77.000 65.000 72.000 72.000 72.000 69.000 73.000 68.000 72.000 78.000 77.000 73.000 73.000 77.000 76.000 76.000 73.000 66.000 73.000 75.000 77.000 70.000 73.000 69.000 70.000 75.000 72.000 73.000 glass 54.545 72.727 77.273 63.636 76.190 85.714 66.667 80.952 66.667 66.667 68.182 86.364 81.818 63.636 66.667 66.667 90.476 61.905 61.905 76.190 72.727 86.364 77.273 63.636 61.905 76.190 57.143 85.714 71.429 66.667 59.091 81.818 72.727 59.091 66.667 76.190 52.381 90.476 71.429 76.190 72.727 77.273 77.273 77.273 80.952 76.190 66.667 80.952 80.952 66.667 86.364 77.273 77.273 77.273 76.190 76.190 57.143 61.905 80.952 61.905 68.182 63.636 77.273 72.727 90.476 90.476 80.952 61.905 66.667 76.190 68.182 72.727 86.364 63.636 61.905 47.619 80.952 76.190 61.905 76.190 72.727 68.182 81.818 77.273 71.429 85.714 71.429 66.667 61.905 76.190 90.909 77.273 72.727 63.636 90.476 66.667 71.429 38.095 66.667 66.667 grub-damage 50.000 43.750 37.500 31.250 25.000 53.333 26.667 33.333 53.333 40.000 43.750 37.500 43.750 31.250 37.500 40.000 26.667 40.000 46.667 20.000 31.250 37.500 50.000 43.750 31.250 53.333 40.000 33.333 40.000 53.333 31.250 43.750 50.000 37.500 37.500 33.333 46.667 46.667 40.000 33.333 62.500 31.250 37.500 43.750 37.500 26.667 33.333 40.000 40.000 40.000 18.750 37.500 50.000 43.750 50.000 20.000 26.667 60.000 40.000 46.667 31.250 37.500 37.500 31.250 56.250 40.000 40.000 40.000 40.000 66.667 18.750 50.000 31.250 68.750 37.500 46.667 26.667 26.667 46.667 40.000 50.000 50.000 56.250 37.500 31.250 46.667 53.333 20.000 46.667 26.667 37.500 43.750 50.000 31.250 56.250 33.333 46.667 40.000 40.000 33.333 haberman 70.968 74.194 70.968 74.194 74.194 74.194 73.333 70.000 73.333 73.333 67.742 74.194 64.516 74.194 74.194 70.968 73.333 73.333 83.333 73.333 74.194 70.968 74.194 74.194 70.968 58.065 73.333 73.333 73.333 73.333 74.194 67.742 74.194 70.968 70.968 70.968 70.000 73.333 73.333 56.667 74.194 74.194 74.194 61.290 74.194 70.968 73.333 73.333 73.333 70.000 74.194 74.194 70.968 64.516 74.194 70.968 73.333 73.333 66.667 73.333 67.742 74.194 74.194 74.194 74.194 67.742 73.333 70.000 60.000 73.333 74.194 74.194 67.742 61.290 74.194 67.742 66.667 73.333 73.333 73.333 74.194 67.742 67.742 67.742 74.194 70.968 73.333 73.333 73.333 73.333 64.516 74.194 70.968 70.968 70.968 70.968 73.333 73.333 73.333 73.333 hayes-roth 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 62.500 56.250 56.250 56.250 56.250 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 62.500 56.250 56.250 56.250 56.250 hepatitis 87.500 68.750 75.000 68.750 81.250 86.667 86.667 73.333 80.000 86.667 75.000 81.250 87.500 68.750 68.750 86.667 80.000 66.667 86.667 86.667 62.500 75.000 81.250 75.000 81.250 86.667 86.667 80.000 73.333 80.000 62.500 81.250 87.500 87.500 50.000 86.667 86.667 80.000 86.667 86.667 93.750 81.250 75.000 93.750 68.750 80.000 80.000 80.000 66.667 73.333 81.250 87.500 75.000 75.000 68.750 80.000 86.667 73.333 93.333 80.000 87.500 87.500 81.250 68.750 87.500 73.333 73.333 80.000 80.000 80.000 81.250 81.250 62.500 81.250 75.000 66.667 73.333 86.667 86.667 80.000 62.500 87.500 81.250 81.250 81.250 80.000 86.667 80.000 80.000 80.000 75.000 81.250 75.000 75.000 68.750 86.667 73.333 86.667 66.667 93.333 hungarian-14 76.667 76.667 76.667 63.333 82.759 86.207 75.862 96.552 72.414 79.310 76.667 80.000 86.667 83.333 82.759 79.310 72.414 79.310 93.103 75.862 76.667 83.333 73.333 80.000 93.103 79.310 82.759 93.103 55.172 72.414 83.333 76.667 76.667 80.000 72.414 86.207 68.966 86.207 89.655 86.207 83.333 86.667 73.333 76.667 86.207 89.655 75.862 79.310 79.310 86.207 76.667 83.333 80.000 90.000 79.310 89.655 75.862 68.966 82.759 82.759 80.000 80.000 80.000 86.667 75.862 82.759 79.310 93.103 68.966 89.655 76.667 76.667 93.333 90.000 82.759 75.862 89.655 75.862 72.414 79.310 76.667 83.333 76.667 83.333 72.414 79.310 79.310 82.759 89.655 82.759 73.333 76.667 76.667 90.000 82.759 82.759 86.207 75.862 72.414 89.655 hypothyroid 99.206 99.206 99.469 99.469 99.204 99.204 99.204 99.204 98.939 99.735 99.206 99.206 98.143 99.204 99.735 99.469 99.735 98.674 98.939 99.204 98.942 98.942 99.469 99.204 99.204 98.939 99.469 99.204 99.204 99.469 98.413 97.884 100.000 98.674 98.939 99.469 99.469 98.674 99.204 99.735 98.942 99.471 98.674 99.469 99.469 99.469 99.735 99.204 99.735 99.204 97.619 99.206 99.735 100.000 99.735 98.674 99.735 98.674 97.878 99.469 99.471 98.942 98.674 99.469 99.735 99.469 100.000 98.674 98.143 98.939 98.413 99.471 99.469 100.000 98.674 99.204 98.939 99.735 98.143 98.939 98.677 99.471 98.939 98.674 100.000 99.735 99.735 99.469 98.674 99.204 99.735 97.884 99.469 98.939 98.939 99.204 100.000 98.408 99.204 99.735 ionosphere 91.667 88.571 85.714 97.143 91.429 88.571 94.286 91.429 94.286 94.286 83.333 88.571 91.429 94.286 100.000 91.429 97.143 88.571 82.857 82.857 94.444 94.286 88.571 88.571 97.143 82.857 97.143 91.429 88.571 85.714 88.889 88.571 94.286 94.286 88.571 91.429 91.429 94.286 85.714 88.571 86.111 100.000 88.571 94.286 88.571 91.429 91.429 82.857 82.857 97.143 88.889 80.000 94.286 88.571 85.714 91.429 85.714 88.571 91.429 80.000 88.889 85.714 97.143 94.286 85.714 97.143 88.571 85.714 97.143 97.143 88.889 85.714 82.857 94.286 85.714 88.571 91.429 85.714 91.429 97.143 86.111 91.429 91.429 94.286 82.857 91.429 88.571 85.714 94.286 91.429 94.444 97.143 85.714 91.429 91.429 85.714 82.857 88.571 88.571 91.429 iris 93.333 100.000 100.000 100.000 86.667 93.333 86.667 86.667 93.333 93.333 93.333 80.000 86.667 100.000 100.000 93.333 100.000 93.333 86.667 100.000 100.000 86.667 93.333 93.333 86.667 100.000 86.667 86.667 93.333 93.333 86.667 93.333 80.000 86.667 100.000 93.333 100.000 100.000 93.333 100.000 100.000 80.000 93.333 100.000 93.333 100.000 93.333 93.333 93.333 93.333 93.333 100.000 93.333 86.667 93.333 93.333 93.333 93.333 100.000 100.000 93.333 86.667 86.667 93.333 93.333 100.000 93.333 93.333 93.333 100.000 86.667 93.333 93.333 100.000 100.000 86.667 93.333 100.000 93.333 86.667 93.333 100.000 86.667 93.333 93.333 100.000 93.333 93.333 93.333 100.000 100.000 100.000 100.000 86.667 93.333 93.333 93.333 86.667 93.333 93.333 kr-s-kp 98.750 99.375 99.062 98.750 100.000 99.688 99.373 99.687 100.000 99.060 99.375 99.062 99.375 99.688 99.375 99.375 99.373 99.373 99.687 99.687 99.688 99.062 99.688 99.688 99.375 98.438 99.687 99.373 99.060 99.373 99.375 99.062 98.750 99.375 99.062 100.000 100.000 99.687 99.373 99.687 99.375 100.000 99.375 99.062 98.750 99.375 99.373 100.000 99.060 99.687 99.375 99.062 99.375 99.688 99.062 99.375 99.373 99.687 99.373 99.373 98.438 99.375 99.062 99.688 99.375 99.688 100.000 100.000 100.000 98.746 99.375 99.688 99.062 99.688 99.375 98.750 99.373 100.000 99.687 99.373 99.375 99.688 99.062 99.062 99.375 99.375 99.373 99.373 99.687 98.746 99.062 99.062 99.688 99.688 100.000 100.000 99.373 99.060 99.060 99.373 labor 66.667 50.000 100.000 50.000 100.000 100.000 83.333 100.000 100.000 100.000 83.333 66.667 100.000 83.333 83.333 100.000 100.000 100.000 80.000 60.000 100.000 83.333 66.667 100.000 100.000 100.000 66.667 80.000 80.000 80.000 100.000 83.333 100.000 66.667 83.333 83.333 66.667 100.000 80.000 60.000 50.000 100.000 83.333 100.000 100.000 50.000 83.333 100.000 100.000 100.000 83.333 100.000 66.667 100.000 83.333 83.333 83.333 100.000 100.000 40.000 83.333 100.000 83.333 50.000 100.000 100.000 66.667 100.000 100.000 80.000 83.333 100.000 100.000 100.000 66.667 100.000 66.667 100.000 40.000 80.000 66.667 83.333 100.000 83.333 100.000 100.000 100.000 80.000 80.000 80.000 83.333 100.000 100.000 83.333 83.333 83.333 100.000 60.000 80.000 100.000 lier-disorders 48.571 57.143 54.286 57.143 57.143 58.824 58.824 55.882 52.941 61.765 57.143 57.143 57.143 57.143 57.143 55.882 55.882 58.824 50.000 58.824 51.429 51.429 62.857 54.286 57.143 58.824 58.824 58.824 55.882 58.824 54.286 62.857 57.143 57.143 62.857 58.824 55.882 58.824 58.824 52.941 51.429 57.143 57.143 60.000 57.143 55.882 58.824 61.765 58.824 50.000 51.429 60.000 42.857 57.143 57.143 58.824 58.824 58.824 50.000 58.824 60.000 57.143 54.286 57.143 57.143 38.235 58.824 55.882 58.824 58.824 54.286 60.000 57.143 57.143 57.143 58.824 61.765 61.765 52.941 58.824 45.714 57.143 51.429 60.000 57.143 58.824 58.824 58.824 61.765 58.824 62.857 62.857 57.143 62.857 48.571 58.824 58.824 50.000 61.765 58.824 lymphography 80.000 73.333 100.000 73.333 86.667 73.333 73.333 80.000 71.429 78.571 93.333 100.000 73.333 73.333 80.000 80.000 73.333 60.000 71.429 85.714 93.333 86.667 93.333 66.667 80.000 80.000 60.000 80.000 71.429 85.714 80.000 60.000 73.333 66.667 80.000 86.667 73.333 73.333 85.714 78.571 73.333 80.000 80.000 93.333 80.000 66.667 73.333 73.333 71.429 92.857 80.000 73.333 80.000 73.333 73.333 73.333 46.667 73.333 78.571 78.571 80.000 73.333 80.000 73.333 86.667 80.000 60.000 66.667 64.286 85.714 86.667 86.667 80.000 80.000 80.000 86.667 86.667 73.333 85.714 71.429 60.000 66.667 66.667 93.333 80.000 80.000 66.667 93.333 85.714 64.286 60.000 93.333 73.333 73.333 60.000 66.667 73.333 86.667 100.000 64.286 monks 65.574 65.000 55.000 65.000 65.000 65.000 66.667 66.667 66.667 66.667 52.459 68.333 66.667 55.000 66.667 65.000 65.000 55.000 65.000 65.000 65.574 56.667 60.000 66.667 66.667 65.000 65.000 65.000 65.000 66.667 62.295 65.000 61.667 65.000 60.000 65.000 73.333 66.667 66.667 66.667 65.574 65.000 65.000 56.667 61.667 65.000 66.667 66.667 56.667 63.333 65.574 58.333 66.667 56.667 66.667 65.000 66.667 65.000 65.000 65.000 65.574 65.000 65.000 65.000 65.000 51.667 66.667 60.000 66.667 66.667 65.574 66.667 66.667 56.667 66.667 65.000 58.333 55.000 65.000 66.667 65.574 65.000 65.000 65.000 65.000 66.667 60.000 56.667 66.667 66.667 65.574 66.667 66.667 66.667 66.667 65.000 65.000 65.000 65.000 65.000 monks1 100.000 100.000 96.429 98.214 98.214 100.000 100.000 96.364 98.182 100.000 100.000 100.000 100.000 92.857 100.000 100.000 98.182 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 91.071 100.000 100.000 92.727 100.000 100.000 91.071 100.000 92.857 100.000 100.000 85.455 100.000 90.909 100.000 100.000 100.000 100.000 100.000 96.429 94.643 94.545 100.000 100.000 94.545 96.429 98.214 98.214 100.000 89.286 100.000 100.000 96.364 100.000 98.182 98.214 89.286 100.000 100.000 98.214 100.000 100.000 90.909 87.273 100.000 100.000 96.429 100.000 94.643 100.000 100.000 100.000 90.909 92.727 96.364 100.000 96.429 100.000 100.000 91.071 100.000 100.000 100.000 100.000 96.364 94.643 100.000 98.214 100.000 100.000 96.429 100.000 100.000 100.000 92.727 monks3 100.000 100.000 96.429 100.000 100.000 100.000 100.000 96.364 100.000 96.364 96.429 96.429 100.000 100.000 98.182 100.000 100.000 98.182 100.000 100.000 100.000 98.214 100.000 96.429 98.182 100.000 98.182 100.000 98.182 100.000 100.000 98.214 100.000 100.000 98.182 100.000 100.000 100.000 96.364 96.364 98.214 100.000 100.000 98.214 96.364 98.182 100.000 98.182 100.000 100.000 100.000 98.214 96.429 100.000 100.000 100.000 100.000 96.364 100.000 98.182 98.214 100.000 100.000 98.214 98.182 100.000 96.364 100.000 100.000 98.182 96.429 100.000 100.000 98.214 100.000 100.000 98.182 98.182 98.182 100.000 98.214 100.000 100.000 96.429 98.182 98.182 98.182 100.000 100.000 100.000 98.214 100.000 98.214 100.000 98.182 100.000 100.000 98.182 98.182 98.182 mushroom 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.000 nursery 97.685 96.991 97.299 96.682 97.685 96.142 96.991 97.377 96.991 96.914 97.299 97.917 97.222 97.145 96.528 97.994 96.605 96.914 97.299 96.836 97.762 97.531 97.608 96.219 97.299 97.840 97.145 96.914 96.836 96.914 97.068 97.917 96.605 97.917 97.454 96.682 97.454 97.222 96.451 97.685 96.759 97.299 97.531 97.145 96.528 98.225 97.531 97.068 96.836 96.682 96.991 97.299 97.917 97.299 97.377 97.068 97.299 97.068 96.682 96.759 97.762 97.377 97.377 97.377 97.608 97.377 96.296 97.762 96.528 96.991 96.296 96.451 97.377 97.531 97.068 98.071 97.377 97.608 96.914 96.836 97.222 97.068 97.840 96.991 97.917 96.914 97.068 97.145 96.991 97.145 96.914 97.608 97.531 96.914 97.222 96.682 97.762 97.762 97.531 96.373 optdigits 80.427 81.851 76.690 81.495 84.698 82.740 81.317 82.384 80.783 77.758 77.402 83.452 83.986 79.715 78.470 79.181 82.918 79.004 79.715 79.893 79.893 80.961 80.249 82.028 80.605 77.936 80.605 77.758 79.715 82.206 82.384 78.648 81.673 80.249 80.071 83.452 77.402 81.317 78.470 79.715 78.470 80.249 80.961 78.826 80.249 80.961 81.495 81.317 77.936 79.715 78.826 78.114 79.715 82.562 83.986 79.359 81.851 81.139 75.623 79.715 81.673 82.206 79.893 76.868 78.114 80.071 78.292 85.053 80.783 81.139 77.936 80.783 83.096 77.936 77.936 81.317 81.139 82.028 79.715 80.961 82.562 77.936 80.071 75.979 82.384 80.071 78.648 77.580 82.740 80.249 79.359 80.249 83.452 79.004 83.986 83.096 82.384 80.071 82.028 79.181 owel 96.168 96.350 96.715 97.258 96.709 96.527 96.161 96.709 97.623 96.344 96.350 96.533 96.350 95.978 96.344 97.075 97.806 95.978 96.709 94.881 96.898 96.898 96.715 95.612 97.075 96.344 95.978 96.709 97.441 96.344 97.080 96.715 97.263 95.064 97.075 96.892 96.344 97.075 96.344 94.881 96.898 96.168 95.620 97.623 95.795 95.978 97.075 96.892 96.161 96.709 97.263 96.898 96.350 96.344 95.064 95.247 96.709 97.806 96.892 97.806 96.533 96.898 96.533 97.441 95.064 97.075 96.344 96.709 97.258 96.709 96.350 96.898 96.533 95.795 96.892 97.258 96.892 95.795 95.795 96.161 96.168 95.073 97.445 95.612 96.161 96.344 95.978 97.258 96.709 97.441 97.080 97.263 96.715 97.258 97.075 97.441 95.247 96.709 96.527 95.612 page-blocks 100.000 75.000 75.000 75.000 50.000 75.000 66.667 66.667 100.000 66.667 50.000 100.000 75.000 75.000 75.000 100.000 33.333 66.667 66.667 66.667 75.000 50.000 50.000 75.000 100.000 100.000 66.667 66.667 100.000 33.333 100.000 75.000 75.000 75.000 75.000 75.000 66.667 66.667 66.667 100.000 75.000 75.000 75.000 75.000 75.000 75.000 66.667 100.000 66.667 66.667 75.000 100.000 100.000 50.000 100.000 100.000 66.667 100.000 66.667 66.667 100.000 75.000 75.000 75.000 75.000 75.000 33.333 66.667 33.333 66.667 75.000 50.000 75.000 75.000 100.000 25.000 66.667 66.667 66.667 66.667 75.000 100.000 50.000 75.000 75.000 100.000 100.000 66.667 100.000 33.333 50.000 50.000 100.000 50.000 75.000 100.000 100.000 66.667 66.667 66.667 pasture-production 88.727 90.273 90.901 89.081 89.991 88.899 90.082 89.263 91.902 89.536 89.545 90.545 90.719 88.171 90.810 90.446 90.173 89.445 88.080 87.989 89.364 90.182 90.264 87.989 89.627 87.898 89.809 90.082 90.355 87.989 89.182 89.000 89.354 91.447 88.444 88.262 90.355 90.628 89.809 90.719 90.455 90.545 90.446 91.083 88.899 89.991 90.628 88.535 88.262 89.081 89.091 90.273 88.990 90.173 91.720 87.989 89.081 88.899 89.172 89.809 89.182 88.455 90.446 89.081 90.810 89.536 89.081 89.445 90.446 89.627 88.636 89.909 89.536 90.264 89.172 90.992 87.352 91.356 88.990 89.172 88.545 89.364 90.446 89.900 89.900 87.989 89.627 89.536 89.809 89.354 89.909 89.909 88.717 87.170 89.718 87.261 90.628 90.082 89.081 89.354 pendigits 76.623 72.727 75.325 77.922 74.026 74.026 70.130 70.130 69.737 75.000 68.831 63.636 76.623 72.727 77.922 80.519 72.727 68.831 76.316 75.000 72.727 71.429 75.325 74.026 83.117 67.532 72.727 75.325 82.895 81.579 81.818 79.221 71.429 70.130 68.831 76.623 75.325 80.519 72.368 72.368 81.818 66.234 77.922 79.221 77.922 79.221 72.727 72.727 72.368 77.632 75.325 75.325 67.532 76.623 68.831 76.623 75.325 83.117 72.368 67.105 66.234 76.623 76.623 64.935 77.922 66.234 80.519 75.325 78.947 82.895 81.818 76.623 74.026 71.429 67.532 79.221 71.429 67.532 71.053 76.316 70.130 75.325 72.727 70.130 64.935 71.429 76.623 83.117 78.947 75.000 70.130 75.325 77.922 74.026 79.221 72.727 77.922 77.922 72.368 61.842 pima-diabetes 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 66.667 66.667 77.778 77.778 77.778 77.778 66.667 55.556 66.667 66.667 66.667 66.667 77.778 77.778 77.778 77.778 66.667 66.667 66.667 66.667 66.667 66.667 77.778 77.778 66.667 77.778 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 55.556 66.667 66.667 77.778 66.667 77.778 77.778 66.667 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 77.778 44.444 66.667 66.667 66.667 66.667 66.667 77.778 77.778 77.778 55.556 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 66.667 44.444 77.778 77.778 77.778 77.778 66.667 66.667 66.667 66.667 77.778 77.778 77.778 77.778 66.667 66.667 postoperatie 38.235 41.176 38.235 50.000 38.235 38.235 41.176 44.118 35.294 36.364 35.294 38.235 38.235 35.294 41.176 44.118 41.176 32.353 50.000 42.424 44.118 41.176 35.294 44.118 38.235 44.118 35.294 35.294 38.235 54.545 35.294 47.059 35.294 35.294 41.176 41.176 41.176 44.118 38.235 36.364 32.353 47.059 52.941 52.941 29.412 47.059 41.176 44.118 41.176 30.303 44.118 35.294 47.059 35.294 38.235 38.235 23.529 41.176 35.294 54.545 26.471 35.294 41.176 47.059 44.118 35.294 50.000 29.412 47.059 48.485 52.941 41.176 55.882 50.000 38.235 44.118 35.294 47.059 38.235 30.303 35.294 35.294 44.118 38.235 44.118 41.176 32.353 47.059 44.118 57.576 47.059 47.059 41.176 35.294 41.176 41.176 38.235 52.941 41.176 36.364 primary-tumor 93.506 95.238 93.506 94.372 93.074 93.939 93.074 93.074 93.074 97.403 94.372 92.208 96.104 92.641 93.074 95.671 91.775 93.074 96.537 95.671 95.238 96.970 95.671 92.641 91.775 95.238 93.939 92.641 92.208 93.074 94.805 94.805 93.074 95.671 93.074 95.238 95.671 94.372 95.671 92.641 90.043 94.372 93.074 96.970 93.074 91.775 91.775 94.372 93.939 95.238 96.537 91.342 93.939 95.238 95.238 90.909 96.537 94.372 93.506 95.671 95.671 92.641 92.641 92.641 94.805 92.208 94.372 96.104 95.238 93.074 92.208 94.372 94.372 91.775 96.104 94.372 94.372 93.939 93.939 93.939 95.671 93.074 92.208 91.775 92.208 93.939 96.104 96.537 94.805 96.970 94.805 93.506 94.372 92.641 93.939 94.805 95.238 93.939 95.238 94.805 segment 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 90.909 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 90.909 87.879 90.625 90.625 90.625 87.500 87.500 87.500 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 87.500 87.500 87.500 90.625 90.625 90.625 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 87.879 87.879 87.879 90.625 90.625 90.625 90.625 87.500 87.500 87.500 solar-flare-C 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 81.250 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 90.909 87.879 87.879 90.625 90.625 90.625 90.625 90.625 90.625 90.625 solar-flare-X 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 96.970 96.970 96.970 100.000 100.000 100.000 96.875 96.875 96.875 96.875 solar-flare-m 85.714 85.714 80.952 76.190 66.667 66.667 71.429 76.190 60.000 75.000 71.429 66.667 80.952 76.190 80.952 71.429 95.238 71.429 85.000 100.000 71.429 66.667 95.238 80.952 80.952 76.190 61.905 61.905 75.000 75.000 71.429 80.952 71.429 66.667 76.190 66.667 57.143 71.429 80.000 90.000 76.190 80.952 85.714 76.190 71.429 71.429 71.429 80.952 85.000 80.000 76.190 61.905 76.190 66.667 76.190 57.143 95.238 90.476 95.000 60.000 80.952 80.952 80.952 80.952 76.190 85.714 71.429 71.429 70.000 75.000 76.190 76.190 71.429 90.476 57.143 71.429 85.714 85.714 65.000 70.000 80.952 76.190 66.667 80.952 76.190 76.190 52.381 76.190 85.000 60.000 76.190 85.714 71.429 42.857 61.905 57.143 71.429 80.952 55.000 75.000 sonar 92.754 92.754 89.855 89.706 95.588 92.647 97.059 94.118 92.647 92.647 88.406 95.652 89.855 95.588 92.647 91.176 92.647 91.176 86.765 97.059 94.203 94.203 92.754 91.176 94.118 92.647 92.647 94.118 97.059 88.235 94.203 95.652 94.203 92.647 89.706 92.647 94.118 97.059 95.588 89.706 92.754 92.754 91.304 94.118 97.059 89.706 91.176 97.059 89.706 92.647 89.855 97.101 94.203 95.588 91.176 95.588 92.647 88.235 95.588 92.647 91.304 92.754 95.652 94.118 92.647 91.176 91.176 97.059 85.294 98.529 92.754 94.203 97.101 94.118 86.765 92.647 89.706 91.176 94.118 97.059 91.304 92.754 92.754 91.176 92.647 92.647 92.647 97.059 94.118 97.059 92.754 92.754 94.203 92.647 92.647 92.647 92.647 94.118 91.176 95.588 soybean 94.360 91.957 92.826 95.000 93.478 92.391 93.478 93.478 92.174 93.043 93.709 93.478 93.261 91.739 93.696 92.391 95.435 93.478 92.609 93.261 93.926 95.217 93.261 93.913 91.739 94.565 94.130 90.652 94.348 93.478 91.106 93.261 94.130 91.087 93.261 95.000 93.696 93.913 93.913 90.652 93.926 91.739 94.130 93.043 92.174 94.130 94.348 95.652 91.739 93.261 91.974 93.696 93.043 92.609 94.565 93.261 93.261 92.826 92.391 94.783 93.492 93.696 93.043 95.652 92.391 91.522 92.391 91.522 95.652 92.174 92.625 93.478 93.478 93.696 95.870 92.826 92.174 91.739 93.913 92.609 93.709 92.609 93.913 95.435 91.957 93.913 92.174 91.087 91.522 93.478 92.625 94.783 93.478 94.565 92.391 92.391 91.957 92.826 94.130 92.826 spambase 85.185 62.963 85.185 77.778 77.778 70.370 85.185 73.077 84.615 80.769 77.778 85.185 85.185 77.778 85.185 88.889 85.185 84.615 92.308 84.615 85.185 85.185 85.185 77.778 88.889 88.889 74.074 92.308 76.923 88.462 81.481 74.074 81.481 70.370 81.481 77.778 88.889 92.308 80.769 92.308 70.370 85.185 77.778 81.481 88.889 81.481 77.778 84.615 88.462 80.769 85.185 74.074 88.889 81.481 77.778 85.185 81.481 80.769 69.231 80.769 74.074 85.185 81.481 81.481 85.185 74.074 77.778 80.769 73.077 88.462 74.074 96.296 74.074 92.593 70.370 85.185 77.778 76.923 73.077 84.615 85.185 85.185 85.185 77.778 66.667 81.481 74.074 84.615 69.231 88.462 85.185 85.185 81.481 85.185 92.593 66.667 77.778 88.462 84.615 84.615 spect-reordered 95.925 94.044 93.417 94.671 94.984 96.238 91.536 92.790 94.984 93.417 93.730 94.671 94.984 91.223 94.357 94.044 94.357 95.611 94.671 94.671 93.730 94.044 93.730 94.357 94.044 94.357 94.671 95.611 94.044 92.163 93.417 93.730 94.044 94.984 95.925 94.984 93.730 92.790 96.238 92.790 93.730 95.298 95.611 92.476 92.790 94.357 95.298 94.671 93.103 91.536 94.357 91.536 93.730 93.730 94.984 96.865 95.925 95.298 94.984 94.671 94.984 93.417 92.476 93.730 92.790 94.044 94.357 94.671 95.298 96.238 93.103 94.984 91.536 94.984 92.790 92.163 92.476 95.925 95.611 94.357 94.044 94.671 93.730 91.850 94.984 94.044 93.417 94.671 94.044 95.611 94.984 93.417 95.611 94.671 95.611 93.417 93.103 92.476 94.357 94.357 splice 50.000 50.000 80.000 80.000 80.000 60.000 60.000 80.000 100.000 60.000 66.667 50.000 80.000 80.000 60.000 100.000 100.000 60.000 20.000 60.000 100.000 66.667 60.000 60.000 20.000 60.000 60.000 60.000 80.000 60.000 83.333 83.333 60.000 60.000 20.000 80.000 40.000 60.000 60.000 60.000 83.333 66.667 60.000 80.000 40.000 80.000 80.000 40.000 20.000 80.000 66.667 33.333 40.000 80.000 80.000 40.000 60.000 40.000 80.000 60.000 66.667 83.333 80.000 40.000 80.000 60.000 80.000 60.000 60.000 60.000 50.000 66.667 40.000 60.000 80.000 80.000 80.000 60.000 60.000 60.000 33.333 50.000 60.000 80.000 80.000 80.000 40.000 80.000 60.000 20.000 66.667 33.333 60.000 40.000 60.000 60.000 80.000 80.000 80.000 80.000 squash-stored 66.667 100.000 100.000 100.000 60.000 80.000 20.000 80.000 60.000 100.000 50.000 83.333 100.000 60.000 100.000 80.000 80.000 100.000 40.000 80.000 83.333 83.333 100.000 100.000 40.000 60.000 60.000 80.000 100.000 60.000 83.333 66.667 80.000 40.000 100.000 80.000 100.000 80.000 80.000 100.000 50.000 83.333 80.000 100.000 100.000 80.000 80.000 80.000 60.000 60.000 83.333 66.667 80.000 60.000 100.000 60.000 80.000 60.000 80.000 100.000 66.667 50.000 100.000 80.000 100.000 80.000 100.000 40.000 60.000 80.000 66.667 0.000 100.000 80.000 100.000 80.000 100.000 100.000 80.000 60.000 66.667 66.667 100.000 80.000 40.000 60.000 100.000 80.000 80.000 80.000 50.000 83.333 80.000 100.000 100.000 100.000 60.000 80.000 80.000 80.000 squash-unstored 50.000 40.000 60.000 46.667 60.000 40.000 40.000 40.000 46.667 46.667 37.500 46.667 60.000 53.333 53.333 40.000 40.000 33.333 60.000 46.667 43.750 40.000 53.333 33.333 53.333 46.667 53.333 46.667 46.667 53.333 37.500 46.667 46.667 40.000 33.333 60.000 40.000 53.333 53.333 60.000 43.750 53.333 46.667 40.000 46.667 46.667 53.333 46.667 53.333 40.000 37.500 53.333 60.000 40.000 33.333 46.667 46.667 53.333 46.667 46.667 31.250 46.667 46.667 40.000 53.333 53.333 53.333 60.000 46.667 40.000 50.000 26.667 46.667 40.000 53.333 53.333 40.000 53.333 40.000 60.000 50.000 46.667 60.000 46.667 46.667 46.667 40.000 40.000 53.333 40.000 43.750 46.667 46.667 46.667 40.000 33.333 46.667 53.333 46.667 53.333 tae 77.778 81.818 71.717 74.747 79.798 81.818 75.758 76.768 78.788 69.697 76.768 82.828 74.747 78.788 84.848 70.707 71.717 83.838 74.747 78.788 79.798 75.758 77.778 79.798 70.707 80.808 67.677 82.828 80.808 78.788 77.778 76.768 81.818 77.778 78.788 77.778 77.778 78.788 81.818 75.758 74.747 85.859 75.758 70.707 70.707 78.788 74.747 79.798 66.667 78.788 75.758 74.747 64.646 79.798 86.869 75.758 77.778 64.646 83.838 78.788 72.727 77.778 75.758 73.737 75.758 74.747 75.758 75.758 84.848 78.788 76.768 76.768 80.808 69.697 79.798 73.737 63.636 72.727 76.768 80.808 74.747 78.788 68.687 73.737 79.798 75.758 75.758 69.697 77.778 84.848 81.818 70.707 79.798 74.747 68.687 78.788 76.768 81.818 75.758 77.778 waveform 75.200 74.200 76.800 72.800 73.000 75.600 76.000 76.000 74.000 75.400 76.200 77.800 76.200 73.600 75.600 78.000 77.200 76.400 72.400 75.000 71.600 73.000 73.800 78.400 74.600 73.600 75.200 76.200 74.200 76.600 76.600 74.600 74.800 78.600 74.000 72.600 77.800 72.800 76.400 76.800 76.600 75.200 76.000 75.000 77.600 75.400 78.200 76.800 76.800 72.200 78.600 73.600 75.400 74.800 77.400 72.800 71.800 77.000 75.200 75.400 75.800 76.400 78.200 73.600 81.000 73.400 77.400 75.800 73.600 76.200 80.000 75.800 76.000 78.400 77.600 77.000 75.600 76.000 75.000 73.200 76.200 75.600 78.000 75.600 76.600 72.200 75.400 74.400 73.000 72.000 74.600 73.800 73.800 75.800 75.000 75.600 78.400 74.000 78.600 76.200 white-clover 71.429 42.857 71.429 83.333 50.000 66.667 50.000 33.333 33.333 66.667 57.143 42.857 57.143 66.667 66.667 66.667 66.667 33.333 33.333 100.000 85.714 85.714 57.143 66.667 33.333 66.667 33.333 66.667 66.667 33.333 57.143 71.429 71.429 66.667 50.000 83.333 16.667 50.000 66.667 66.667 100.000 71.429 71.429 50.000 50.000 83.333 66.667 66.667 33.333 50.000 71.429 42.857 71.429 83.333 83.333 33.333 83.333 50.000 66.667 66.667 57.143 42.857 71.429 83.333 66.667 50.000 33.333 66.667 66.667 16.667 57.143 100.000 57.143 66.667 83.333 66.667 50.000 50.000 33.333 50.000 71.429 100.000 71.429 66.667 50.000 66.667 50.000 66.667 50.000 66.667 71.429 71.429 57.143 66.667 66.667 66.667 50.000 66.667 83.333 16.667 wine 88.889 88.889 88.889 77.778 100.000 83.333 94.444 88.889 94.118 88.235 83.333 88.889 88.889 94.444 100.000 94.444 94.444 100.000 76.471 88.235 100.000 88.889 88.889 88.889 88.889 88.889 88.889 94.444 94.118 88.235 94.444 100.000 94.444 94.444 94.444 83.333 83.333 83.333 100.000 82.353 94.444 94.444 88.889 88.889 94.444 100.000 88.889 94.444 88.235 100.000 83.333 83.333 94.444 88.889 94.444 88.889 100.000 94.444 94.118 82.353 88.889 100.000 83.333 88.889 94.444 94.444 94.444 88.889 88.235 82.353 77.778 94.444 88.889 94.444 83.333 100.000 83.333 94.444 94.118 88.235 100.000 94.444 88.889 94.444 94.444 83.333 100.000 88.889 94.118 94.118 88.889 100.000 94.444 83.333 88.889 94.444 83.333 83.333 94.118 88.235 wisconsin-breast-cancer 98.571 91.429 97.143 85.714 90.000 95.714 92.857 95.714 97.143 92.754 94.286 95.714 97.143 92.857 100.000 94.286 98.571 90.000 94.286 100.000 100.000 94.286 92.857 95.714 91.429 97.143 98.571 97.143 91.429 98.551 95.714 95.714 95.714 98.571 87.143 92.857 95.714 97.143 95.714 95.652 97.143 92.857 94.286 90.000 95.714 97.143 98.571 97.143 95.714 94.203 94.286 100.000 95.714 94.286 94.286 97.143 98.571 92.857 94.286 97.101 94.286 95.714 97.143 91.429 95.714 95.714 95.714 95.714 92.857 100.000 92.857 98.571 100.000 98.571 92.857 91.429 95.714 94.286 95.714 97.101 97.143 98.571 94.286 94.286 97.143 92.857 92.857 95.714 95.714 92.754 92.857 92.857 97.143 97.143 90.000 94.286 98.571 98.571 97.143 94.203 yeast 59.060 54.362 57.047 59.060 52.027 58.108 60.135 61.486 55.405 53.378 59.732 60.403 55.705 53.020 62.162 55.405 45.946 54.054 58.108 63.514 56.376 62.416 59.060 55.034 60.811 57.432 56.081 52.027 60.811 57.432 57.047 57.047 51.007 61.074 55.405 59.459 59.459 51.351 61.486 54.730 55.034 57.047 61.745 53.691 52.027 54.730 56.757 56.757 55.405 56.081 53.020 56.376 56.376 59.732 53.378 62.162 55.405 60.135 54.054 64.865 57.718 56.376 57.718 57.718 58.108 60.135 52.703 55.405 58.108 56.081 52.349 60.403 61.074 52.349 59.459 54.054 56.757 62.162 52.703 61.486 51.678 57.047 62.416 58.389 53.378 60.135 53.378 54.730 67.568 53.378 59.732 59.060 55.705 55.034 57.432 62.162 58.108 58.108 53.378 56.757 zoo 81.818 100.000 100.000 80.000 90.000 100.000 80.000 100.000 90.000 100.000 90.909 90.000 90.000 90.000 100.000 80.000 100.000 100.000 100.000 90.000 90.909 100.000 100.000 100.000 80.000 100.000 90.000 80.000 100.000 90.000 90.909 100.000 100.000 90.000 100.000 80.000 90.000 90.000 90.000 90.000 100.000 100.000 90.000 100.000 90.000 90.000 80.000 90.000 90.000 90.000 81.818 100.000 90.000 100.000 90.000 90.000 90.000 100.000 100.000 90.000 90.909 90.000 100.000 90.000 90.000 100.000 90.000 90.000 100.000 100.000 90.909 100.000 100.000 90.000 100.000 80.000 100.000 70.000 100.000 90.000 81.818 90.000 100.000 100.000 100.000 80.000 80.000 90.000 100.000 100.000 90.909 100.000 90.000 80.000 100.000 100.000 100.000 90.000 80.000 90.000 baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/code/data.py000066400000000000000000000033131445677601600236200ustar00rootroot00000000000000import os from functools import lru_cache import numpy as np @lru_cache(1) def _read_data(): data = [] datasets = [] classifiers = [] basedir = os.path.split(__file__)[0] with open(os.path.join(basedir, "accuracies.txt")) as f: classifier = f.readline().strip() while True: # loop over classifier if not classifier: break classifiers.append(classifier) data.append([]) t_datasets = datasets and [] while True: # loop over data sets line = f.readline().strip() dataset, *scores = line.split() if line else ("",) if not scores: # Check that order of data sets is same for all classifiers assert datasets == t_datasets classifier = dataset break data[-1].append([float(x) for x in scores]) t_datasets.append(dataset) return np.array(data), classifiers, datasets def get_data(classifier=..., dataset=..., aggregate=False): def get_indices(names, pool): if names is ...: return np.arange(len(pool), dtype=int) if isinstance(names, str): return np.array([pool.index(names)]) else: return np.array([pool.index(name) for name in names]) data, classifiers, datasets = _read_data() data = data[np.ix_(get_indices(classifier, classifiers), get_indices(dataset, datasets))] if aggregate: data = np.mean(data, axis=2) data = data.squeeze() return data def get_classifiers(): return _read_data()[1] def get_datasets(): return _read_data()[2] baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/code/demo.py000066400000000000000000000015501445677601600236340ustar00rootroot00000000000000import matplotlib.pyplot as plt import baycomp as bc from data import get_data data_nbc = get_data("nbc", "squash-unstored") data_aode = get_data("aode", "squash-unstored") data_nbc = get_data("nbc", aggregate=True) data_aode = get_data("aode", aggregate=True) data_j48 = get_data("j48", aggregate=True) t = bc.SignTest(data_nbc, data_aode, 1) print(t.probs()) t.plot() print(bc.SignTest.probs(data_nbc, data_aode, 1)) bc.SignTest.plot(data_nbc, data_aode, 1) data_nbc = get_data("nbc") data_aode = get_data("aode") print(bc.two_on_multiple(data_nbc, data_aode, 0.1, runs=10)) sample = bc.HierarchicalTest.sample(data_nbc, data_aode, 0.3, runs=10) bc.HierarchicalTest.plot(data_nbc, data_aode, 0.3, runs=10, names=("nbc", "aode")) sample = bc.HierarchicalTest(data_nbc, data_aode, 0.3, runs=10) sample.plot(names=("nbc", "aode")) print(sample.probs()) plt.show() baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/conf.py000066400000000000000000000130111445677601600227160ustar00rootroot00000000000000#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # baycomp documentation build configuration file, created by # sphinx-quickstart on Thu Feb 1 22:01:32 2018. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', 'sphinx.ext.napoleon'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # General information about the project. project = 'baycomp' copyright = '2018, Janez Demsar, Alessio Benavoli, Giorgio Corani' author = 'Janez Demsar, Alessio Benavoli, Giorgio Corani' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1.0' # The full version, including alpha/beta/rc tags. release = '1.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinxdoc' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # This is required for the alabaster theme # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars html_sidebars = { '**': [ 'globaltoc.html', # 'relations.html', # needs 'show_related': True theme option to display # 'searchbox.html', ] } # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'baycompdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'baycomp.tex', 'baycomp Documentation', 'Janez Demsar, Alessio Benavoli, Giorgio Corani', 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'baycomp', 'baycomp Documentation', [author], 1) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'baycomp', 'baycomp Documentation', author, 'baycomp', 'One line description of project.', 'Miscellaneous'), ] autodoc_member_order = 'bysource' import sys from unittest.mock import MagicMock class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() MOCK_MODULES = ['numpy', 'scipy'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) import baycompbaycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/functions.rst000066400000000000000000000070421445677601600241700ustar00rootroot00000000000000.. currentmodule:: baycomp Shortcut functions ================== Single data set --------------- The simplest way to compare two classifiers on a single data set is to call a function :obj:`two_on_single`. .. autofunction:: two_on_single Let `nbc` and `j48` be `numpy` arrays with classification accuracies of naive Bayesian classifier and J48 obtained by cross-validation on some data set. Function :obj:`two_on_single` computes the probability that NBC is better than J48 and vice versa. >>> two_on_single(nbc, j48) (0.4145119975061462, 0.5854880024938538) There is a 58.5 % probability that the average performance of J48 on this problem is better than that of NBC. We can add rope: let us consider the two methods equivalent if their classification accuracies differ by less than 0.5. (Recall again that `rope` is on the same scale as `nbc` and `j48`. If they are in percents, so is the rope.) >>> two_on_single(nbc, j48, rope=0.5) (0.28584464173791002, 0.2691518716880249, 0.44500348657406508) There is a 26.9 % chance that the difference between the two classifiers is negligible (that is, smaller than 0.5). There is 44.5 % probability that J48 is better than NBC and 28.6 that NBC is better. We can observe the posterior distribution of differences graphically. With an additional argument `plot=True`, the function returns the probabilities and a density plot, which we can save to a file. >>> names = ("nbc", "j48") >>> probs, fig = two_on_single(nbc, j48, plot=True, names=names) >>> print(probs) (0.4145119975061462, 0.5854880024938538) >>> fig.savefig("t-no-rope.svg") .. image:: _static/t-norope.svg :width: 400px The printed probabilities, `(0.4145119975061462, 0.5854880024938538)`, equal the areas to the left and right of the orange line that marks the zero difference. >>> probs, fig = two_on_single(nbc, j48, rope=0.5, plot=True, names=names) >>> print(probs) (0.28584464173791002, 0.2691518716880249, 0.44500348657406508) >>> fig.savefig("t.svg") .. image:: _static/t.svg :width: 400px With rope, we get three probabilities, `(0.28584464173791002, 0.2691518716880249, 0.44500348657406508)`, that correspond to areas to the left of rope, within it (between the orange lines) and right of rope. Multiple data sets ------------------ Function :obj:`two_on_multiple` compares classifiers tested on multiple data sets. .. autofunction:: two_on_multiple Let `nbc` and `j48` now contain average performances of the two methods for all data sets. In our case, we have 54 data sets, so `nbc` and `j48` are 1-dimensional arrays with 54 elements. The following code uses a Bayesian version of signed-ranks (Benavoli et al, 2014) to compute the posterior distribution. :: >>> two_on_multiple(nbc, j48, rope=1) (0.23014, 0.00674, 0.76312) There is 76.3 % probability that J48 is better than NBC. >>> probs, fig = two_on_multiple(nbc, j48, rope=1, plot=True, names=names) >>> fig.savefig("signedrank.png") .. image:: _static/signedrank.png :width: 400px This test is computed from averages on data sets. We can also pass the entire data, that is, `nbc` and `j48` as 54x10 matrices containing 10 scores for each of 54 data sets. The function will switch from signed-ranks test to a hierarchical model (Corani et al, 2015). >>> probs, fig = two_on_multiple(nbc, j48, rope=1, plot=True, names=names) (0.0795, 0.0365, 0.884) .. image:: _static/hierarchical.png :width: 400px If the results are obtained by multiple runs of cross validation, we must not forget to specify the number of runs. baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/index.rst000066400000000000000000000051461445677601600232720ustar00rootroot00000000000000baycomp ======= Baycomp is a library for Bayesian comparison of classifiers. (For those who don't know what they do and how, and why you should use them instead of testing null hypotheses, we prepared a :doc:`short introduction for dummies `). Functions in the library compare two classifiers on one or on multiple data sets. They compute three probabilities: the probability that the first classifier has higher scores than the second, the probability that differences are within the region of practical equivalence (rope), or that the second classifier has higher scores. We will refer to this probabilities as `p_left`, `p_rope` and `p_right`. If the argument `rope` is omitted (or set to zero), functions return only `p_left` and `p_right`. The region of practical equivalence (rope) is specified by the caller and should correspond to what is "equivalent" in practice; for instance, classification accuracies that differ by less than 1 % may be called equivalent. So we can set a rope of 1 (if accuracies are on scale from 0 to 100, or to 0.01, if they are on a scale from 0 to 1). Similarly, whether higher scores are better or worse depends upon the type of the score. The library can also plot the posterior distributions. The library can be used in three ways. 1. Two :doc:`shortcut functions ` can be used for comparison on single and on multiple data sets. If `nbc` and `j48` contain a list of average classification accuracies of naive Bayesian classifier and J48 on a collection of data sets, we can call >>> two_on_multiple(nbc, j48, rope=1) (0.23124, 0.00666, 0.7621) (Actual outputs may differ due to Monte Carlo sampling.) With some additional arguments, the function can also plot the posterior distribution from which these probabilities came. 2. Tests are packed into :doc:`test classes `. The above call is equivalent to >>> SignedRankTest.probs(nbc, j48, rope=1) (0.23124, 0.00666, 0.7621) and to get a plot, we call >>> SignedRankTest.plot(nbc, j48, rope=1, names=("nbc", "j48")) To switch to another test, use another class:: >>> SignTest.probs(nbc, j48, rope=1) (0.26508, 0.13274, 0.60218) 3. Finally, we can construct and query sampled :doc:`posterior distributions `. >>> posterior = SignedRankTest(nbc, j48, rope=1) >>> posterior.probs() (0.23124, 0.00666, 0.7621) >>> posterior.plot(names=("nbc", "j48")) Detailed documentation is given on the following pages. .. toctree:: :maxdepth: 2 functions classes posterior introduction baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/introduction.rst000066400000000000000000000140311445677601600246750ustar00rootroot00000000000000Bayesian comparison of learning algorithms for dummies ======================================================== What are the test about? ------------------------ This time, let us start with comparison of multiple classifiers. Say that we have compared algorithms A and B on 50 data sets; algorithm A was better on 30, and B won on 20. Our goal is to determine the probability that given a new data set (of a similar kind as data sets on which we compared the classifiers so far) A will perform better than B (and the opposite). With A being better on 30 data sets, we can - without any fancy Bayesian stuff - say that the probability of A being indeed better (on this kind of data sets) is 0.6, and the probability that B is better is 0.4. But is it really? What we have is only a single measurement on some specific 50 data sets out of many other possible data sets of reasonably similar kind. A single sample, so to speak. A single sample giving a single result - 0.6 for A and 0.4 for B. This sample comes from some distribution; if we repeated this experiment (on the same or other data sets), the result could have been different, say 0.55 for A and 0.45 for B. Or (way less likely) 0.2 for A and 0.8 for B. Or ... maybe it is *our result*, 0.6 vs 0.4 that was unlikely? We cannot know. With a Bayesian approach, we start with a *prior distribution*. A reasonable prior would state that the two algorithms are equal, and that our belief in this equality is of such and such strength. After making experiments (like above), we adapt our prior distribution accordingly and get a *posterior distribution*. This is then a distribution for which we believe that our single sample (0.6 vs. 0.4) was drawn. Sometimes we can deal with posterior distributions analytically. When not (comparison on multiple data sets is a case of a "*not*", while for a single data set we can do it), we use Monte Carlo sampling. We produce a large number (`baycomp`'s default is 50000) samples from the posterior distribution. Every sample gives the probability of A being better; most will be close to 0.6, but they will spread around and in some cases go below 0.5, which means that this sample predicts that B is in fact better than A. First thing we can do is to count the number of samples on which A was better. If it was better on 27000 out of 50000 samples, than we predict that there is a 27000 / 50000 = 0.54 chance that A is better than B. Second, we can observe the distribution of samples. We can for plot it, as a histogram ... or do something even better, as we'll see later. And for a single data set? -------------------------- The story there is similar. We have two classifiers, we measure differences in their performance using cross-validation and the average difference was, say, 2.5 if favour of A. If we repeated the experiment, we could have gotten various differences - the difference will sometimes will rise to 3, sometimes it will be as low as 0.5 and sometimes B will even win. Now, imagine that we draw 50000 samples, like above, and count how many times A won. If the difference was positive (in favour of A) in 30000 cases, there is a 60 % probability that A is better. Except that here we don't have to draw 50000 samples since we can model the distribution analytically and compute the expected number of wins for A. This is more accurate and also faster. .. image:: _static/t-norope.svg :width: 400px How is this different from testing null-hypotheses? --------------------------------------------------- When the null-hypothesis test gives a p-value of 0.05, this means that there is only a 5 % probability of getting such (or more) extreme differences if the classifiers are equal. This is thus the probability of the data and does not tell anything about the probability that A is better then B. Once again: no, getting a p-value of 0.05 doesn't mean that there is a 95 % chance that A is better than B. Bayesian tests give us exactly that: the actual probability of A being better than B. This is only the practical aspect of the difference. (And you'll see one more below.) There are however plenty of reasons why null-hypothesis testing is not only impractical but also plain wrong. And what about the rope? ------------------------ This is the best thing about Bayesian tests: we don't get just a number, we get a distribution, which we can query. One practical way to use it is this one: would you consider a classifier A better than B, if the accuracy of A is 85.14 % and the accuracy of B is 85.13 %? It surely is, but the difference is negligible in practice, right? (Is this statistically significant? It depends upon the sample size. Collect enough data, and this difference will become significant.) With Bayesian tests, we can decide for a *region of practical equivalence*, or *rope*. The size of the rope depends on the use case. Say that we would consider a pair of classifier that differ by less than 0.5 equivalent. If so, we have three possibilities. Either A is better than B by more than 0.5; or B is better than A by more than 0.5; or the difference is within rope, that is, smaller than 0.5. Now, we can go back and revisited what we said above. In case of comparisons on multiple data sets, we gave an example in which "*we have compared algorithms A and B on 50 data sets; algorithm A was better on 30, and B won on 20*" and that hence "*the probability of A being indeed better (...) is 0.6, and the probability that B is better is 0.4.*" Now the example may change to something like "algorithm A was better on 25, B was better on 10 and they were within 0.5 percent in the remaining 15", so "the probability that A is better is 0.5, the probability that B is better is 0.2, and they are equivalent with a probability of 0.3". Similar story for a single data set: we can compute the distribution of differences and instead of counting the cases in which A or B won (or computing the areas to the left and the right of zero in the density plot), we split the cases or the plot into three regions, the middle one containing the negligible differences. .. image:: _static/t.svg :width: 400px baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/make.bat000066400000000000000000000014531445677601600230330ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=_build set SPHINXPROJ=baycomp if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/docs/posterior.rst000066400000000000000000000026611445677601600242100ustar00rootroot00000000000000.. currentmodule:: baycomp Querying posterior distributions ================================ The third way to use the library is to construct and query posterior distributions. We construct the posterior distribution by calling the corresponding test class. If `j48` and `nbc` contain scores from cross validation on a single data set, we construct the posterior by >>> posterior = CorrelatedTTest(nbc, j48) and then compute the probabilities and plot the histogram >>> posterior.probs() (0.4145119975061462, 0.5854880024938538) >>> fig = posterior.plot(names=("nbc", "j48")) For comparison on multiple data sets we do the same, except that `nbc` and `j48` must contain average classification accuracies (for sign test and signed rank test) or a matrix of accuracies (for hierarchical test). >>> posterior = SignedRankTest(nbc, j48, rope=1) >>> posterior.probs() (0.23014, 0.00674, 0.76312) >>> fig = posterior.plot(names=("nbc", "j48")) Single data set --------------- .. autoclass:: baycomp.single.Posterior :members: Unlike the posterior for comparisons on multiple data sets, this distribution is not sampled; probabilities are computed from the posterior Student distribution. The class can provide a sample (as 1-dimensional array), but the sample itself is not used by other methods. Multiple data sets ------------------ .. autoclass:: baycomp.multiple.Posterior :members: baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/setup.py000066400000000000000000000020311445677601600222010ustar00rootroot00000000000000from setuptools import setup, find_packages with open("README.md", "r") as fh: long_description = fh.read() setup( name='baycomp', version='1.0.3', url='https://github.com/janezd/baycomp.git', author='J. Demsar, A. Benavoli, G. Corani', author_email='janez.demsar@fri.uni-lj.si', description='Bayesian tests for comparison of classifiers', long_description=long_description, long_description_content_type="text/markdown", classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Science/Research', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3' ], packages=find_packages(), install_requires=[ 'matplotlib >= 2.1.2', 'numpy >= 1.13.1', 'scipy >= 0.19.1'], extra_requires=[ 'pystan >= 3.4.0' ], python_requires='>=3', package_data={ 'baycomp': ['hierarchical-t-test.stan']} ) baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/tests/000077500000000000000000000000001445677601600216355ustar00rootroot00000000000000baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/tests/__init__.py000066400000000000000000000000001445677601600237340ustar00rootroot00000000000000baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/tests/test_multiple.py000066400000000000000000000156361445677601600251140ustar00rootroot00000000000000import unittest from unittest.mock import patch, Mock import numpy as np from tests.utils import TestTestBase from baycomp.multiple import \ SignTest, SignedRankTest, HierarchicalTest, two_on_multiple, \ Test, Posterior class PosteriorTest(unittest.TestCase): def test_probs(self): sample = np.array([[0.2, 0.1, 0.7], [0.4, 0.1, 0.5], [0.1, 0.1, 0.8], [0.9, 0.0, 0.1], [0.8, 0.1, 0.1], [0, 1, 0]]) posterior = Posterior(sample) np.testing.assert_almost_equal( np.array(posterior.probs()), np.array([2, 1, 3]) / 6) np.testing.assert_almost_equal( np.array(posterior.probs(True)), np.array([2, 1, 3]) / 6) np.testing.assert_almost_equal( np.array(posterior.probs(False)), np.array([2, 3]) / 5) @patch.object(Posterior, "plot_histogram") @patch.object(Posterior, "plot_simplex") def test_plot(self, mocksimplex, mockhistogram): names = ("a", "b") names2 = ("c", "d") sample = np.array([[0.2, 0.2, 0.6], [0.1, 0, 0.9]]) posterior = Posterior(sample, names=names) posterior.plot() mocksimplex.assert_called_with(None) mockhistogram.assert_not_called() posterior.plot(names2) mocksimplex.assert_called_with(names2) mockhistogram.assert_not_called() mocksimplex.reset_mock() sample = np.array([[0.35, 0.05, 0.6], [0.1, 0, 0.9]]) posterior = Posterior(sample, names=names) posterior.plot() mockhistogram.assert_called_with(None) mocksimplex.assert_not_called() posterior.plot(names2) mockhistogram.assert_called_with(names2) mocksimplex.assert_not_called() class TestTest(TestTestBase): @patch.object(Test, "sample", return_value=Mock()) @patch("baycomp.multiple.Posterior") def test_new(self, mockposterior, mocksample): x = np.array([4, 2, 6]) y = np.array([5, 7, 3]) Test(x, y, 1, nsamples=3, foo=42) mocksample.assert_called_with(x, y, 1, nsamples=3, foo=42, random_state=None) mockposterior.assert_called_with(mocksample.return_value) def test_probs(self): x = np.array([4, 2, 6]) y = np.array([5, 7, 3]) self.assert_forwards( Test, "probs", x, y, 0.5, nsamples=42, new_args=(x, y, 0.5), new_kwargs=dict(nsamples=42), meth_args=(True, ) ) self.assert_forwards( Test, "probs", x, y, 0, nsamples=42, new_args=(x, y, 0), new_kwargs=dict(nsamples=42), meth_args=(False, ) ) self.assert_forwards( Test, "probs", x, y, nsamples=42, new_args=(x, y, 0), new_kwargs=dict(nsamples=42), meth_args=(False, ) ) def test_plot(self): x = np.array([4, 2, 6]) y = np.array([5, 7, 3]) names = ("a", "b") self.assert_forwards( Test, "plot", x, y, 0.5, nsamples=42, names=names, new_args=(x, y, 0.5), new_kwargs=dict(nsamples=42), meth_args=(names, ) ) self.assert_forwards( Test, "plot_simplex", x, y, 0.5, nsamples=42, names=names, new_args=(x, y, 0.5), new_kwargs=dict(nsamples=42), meth_args=(names, ) ) self.assert_forwards( Test, "plot_histogram", x, y, nsamples=42, names=names, new_args=(x, y), new_kwargs=dict(nsamples=42, rope=0), meth_args=(names, ) ) class SignTestTest(unittest.TestCase): @patch("numpy.random.RandomState") def test_sample(self, mockrandomstate): x = np.array([15, 16, 17, 24, 11, 12, 13, 14]) y = np.array([10, 13, 15, 24, 15, 82, 83, 84]) # diff = -5 -3 -2 0 4 70 70 70 def assert_dirichlet(s, nsamples=50000): alpha, ns = mockrandomstate.mock_calls[-1].args np.testing.assert_almost_equal(alpha, np.array(s) + 0.0001) self.assertEqual(ns, nsamples) SignTest.sample(x, y, prior=0) assert_dirichlet([3, 1, 4]) SignTest.sample(x, y, prior=0, rope=1) assert_dirichlet([3, 1, 4]) SignTest.sample(x, y, prior=0, rope=2) assert_dirichlet([2, 2, 4]) SignTest.sample(x, y, prior=0, rope=3) assert_dirichlet([1, 3, 4]) SignTest.sample(x, y, prior=0, rope=4) assert_dirichlet([1, 4, 3]) SignTest.sample(x, y, prior=0, rope=5) assert_dirichlet([0, 5, 3]) SignTest.sample(x, y, prior=0, rope=100) assert_dirichlet([0, 8, 0]) SignTest.sample(x, y, prior=0.5, rope=1) assert_dirichlet([3, 1.5, 4]) SignTest.sample(x, y, prior=(0.5, SignTest.LEFT), rope=1) assert_dirichlet([3.5, 1, 4]) SignTest.sample(x, y, prior=(0.5, SignTest.ROPE), rope=1) assert_dirichlet([3, 1.5, 4]) SignTest.sample(x, y, prior=(0.5, SignTest.RIGHT), rope=1) assert_dirichlet([3, 1, 4.5]) SignTest.sample(x, y, prior=0, nsamples=42) assert_dirichlet([3, 1, 4], 42) @patch("baycomp.multiple.check_args") @patch("numpy.random.dirichlet") def test_sample_checks_args(self, _, mockcheckargs): x = np.array([15, 16, 17, 24, 11, 12, 13, 14]) y = np.array([10, 13, 15, 24, 15, 82, 83, 84]) SignTest.sample(x, y, rope=1, prior=0.5, nsamples=42) mockcheckargs.assert_called_with(x, y, 1, 0.5, 42) class SignedRankTestTest(unittest.TestCase): def test_sample(self): # TODO This tests only that sampling does not crash x = np.array([15, 16, 17, 24, 11, 12, 13, 14]) y = np.array([10, 13, 15, 24, 15, 82, 83, 84]) def test(*args, **kwargs): self.assertEqual( SignedRankTest.sample(*args, nsamples=10, **kwargs).shape, (10, 3)) test(x, y) test(x, y, rope=1) test(x, y, rope=3) test(x, y, rope=100) test(x, y, prior=5) test(x, y, prior=5, rope=10) class TwoOnMultipleTest(unittest.TestCase): @patch("baycomp.multiple.call_shortcut") def test_two_on_multiple(self, mockcall): names = ("a", "b") x = np.array([1, 2, 3]) y = np.array([4, 5, 6]) two_on_multiple(x, y, 0.5, runs=10, plot=True, names=names) mockcall.assert_called_with( SignedRankTest, x, y, 0.5, plot=True, names=names) x = np.array([[1, 2, 3], [4, 5, 6]]) y = np.array([[4, 5, 6], [1, 2, 3]]) two_on_multiple(x, y, 0.5, plot=True, names=names) mockcall.assert_called_with( HierarchicalTest, x, y, 0.5, plot=True, runs=1, names=names) two_on_multiple(x, y, 0.5, runs=10, plot=True, names=names) mockcall.assert_called_with( HierarchicalTest, x, y, 0.5, plot=True, runs=10, names=names) if __name__ == "__main__": unittest.main() baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/tests/test_single.py000066400000000000000000000127051445677601600245340ustar00rootroot00000000000000import unittest from unittest.mock import patch import numpy as np from tests.utils import TestTestBase from baycomp.single import CorrelatedTTest, Posterior, two_on_single class PosteriorTest(unittest.TestCase): def test_sample(self): posterior = Posterior(42, 0, 1, nsamples=10) np.testing.assert_almost_equal(posterior.sample, np.full((10, ), 42)) posterior = Posterior(42, 2, 3, nsamples=10) def mockstandardt(df, nsamples): return np.arange(nsamples) * df with patch("numpy.random.standard_t", mockstandardt): np.testing.assert_almost_equal( posterior.sample, 42 + np.sqrt(2) * np.arange(10) * 3) def test_probs_var_0(self): # var=0, df=1, rope=2 np.testing.assert_equal(Posterior(42, 0, 1, 2).probs(), [0, 0, 1]) np.testing.assert_equal(Posterior(2, 0, 1, 2).probs(), [0, 1, 0]) np.testing.assert_equal(Posterior(.5, 0, 1, 2).probs(), [0, 1, 0]) np.testing.assert_equal(Posterior(0, 0, 1, 2).probs(), [0, 1, 0]) np.testing.assert_equal(Posterior(-.5, 0, 1, 2).probs(), [0, 1, 0]) np.testing.assert_equal(Posterior(-2, 0, 1, 2).probs(), [0, 1, 0]) np.testing.assert_equal(Posterior(-42, 0, 1, 2).probs(), [1, 0, 0]) # var=0, df=1, rope=0 np.testing.assert_equal(Posterior(42, 0, 1).probs(), [0, 1]) np.testing.assert_equal(Posterior(0, 0, 1).probs(), [0.5, 0.5]) np.testing.assert_equal(Posterior(-42, 0, 1).probs(), [1, 0]) def test_probs(self): def mockcdf(x, a_df, a_loc, a_scale): self.assertEqual(a_df, 1) self.assertEqual(a_loc, 3) self.assertAlmostEqual(a_scale, 5) # 0.25 below rope (or 0) # 0.4 below + between return 0.25 if x <= 0 else 0.4 with patch("scipy.stats.t.cdf", mockcdf): posterior = Posterior(mean=3, var=25, df=1, rope=0) np.testing.assert_almost_equal( posterior.probs(), [0.25, 0.75]) posterior = Posterior(mean=3, var=25, df=1, rope=2) np.testing.assert_almost_equal( posterior.probs(), [0.25, 0.15, 0.6]) class CorrelatedTTestTest(TestTestBase): def test_new(self): x = np.array([4, 2, 6]) y = np.array([5, 7, 3]) with patch.object(CorrelatedTTest, "compute_statistics", return_value=(1, 2, 3)) as cs: posterior = CorrelatedTTest(x, y, rope=6, runs=3, names=("a", "b")) cs.assert_called_with(x, y, 3) self.assertEqual(posterior.mean, 1) self.assertEqual(posterior.var, 2) self.assertEqual(posterior.df, 3) self.assertEqual(posterior.rope, 6) self.assertEqual(posterior.meanx, 4) self.assertEqual(posterior.meany, 5) self.assertEqual(posterior.names, ("a", "b")) def test_new_checks_errors(self): x = np.array([4, 2, 6]) y = np.array([5, 7, 3]) with patch("baycomp.single.check_args") as ca: CorrelatedTTest(x, y, 12) ca.assert_called_with(x, y, 12) self.assertRaises(ValueError, CorrelatedTTest, x, y, runs=0) self.assertRaises(ValueError, CorrelatedTTest, x, y, runs=2) self.assertRaises(ValueError, CorrelatedTTest, x, y, runs=-1) self.assertRaises(ValueError, CorrelatedTTest, x, y, runs=0.5) def test_compute_statistics(self): x = np.array([4, 2, 6]) y = np.array([5, 7, 3]) self.assertEqual( CorrelatedTTest.compute_statistics(x, y, 1), (1, (4 ** 2 + 4 ** 2) / 2 * (1 / 3 + 1 / 2), 2)) x = np.array([4, 2, 6, 4]) y = np.array([5, 7, 3, 5]) self.assertEqual( CorrelatedTTest.compute_statistics(x, y, 2), (1, (4 ** 2 + 4 ** 2) / 3 * (1 / 4 + 1), 3)) x = np.ones(10) y = np.zeros(10) self.assertEqual( CorrelatedTTest.compute_statistics(x, y), (-1, 0, 9)) def test_sample(self): x = np.array([42, 42, 42]) y = np.zeros(3) np.testing.assert_almost_equal( CorrelatedTTest.sample(x, y, 1, nsamples=10), np.full((10, ), -42)) def mockstandardt(df, nsamples): return np.arange(nsamples) * df with patch("numpy.random.standard_t", mockstandardt),\ patch.object(CorrelatedTTest, "compute_statistics", return_value=(1, 2, 3)): np.testing.assert_almost_equal( CorrelatedTTest.sample(x, y, 1, nsamples=10), 1 + np.sqrt(2) * np.arange(10) * 3) def test_probs(self): x, y = object(), object() self.assert_forwards(CorrelatedTTest, "probs", x, y, 2, 1) def test_plot(self): x, y = object(), object() names = object() self.assert_forwards( CorrelatedTTest, "plot", x, y, 2, 1, names=names, new_args=(x, y, 2, 1), new_kwargs={}, meth_args=(names,)) class TwoOnSingleTest(unittest.TestCase): def test_two_on_single(self): x, y = object(), object() names = ("a, b") with patch("baycomp.single.call_shortcut") as mockshortcut: two_on_single(x, y, 0.5, 10, plot=True, names=names) mockshortcut.assert_called_with(CorrelatedTTest, x, y, 0.5, plot=True, names=names, runs=10) if __name__ == "__main__": unittest.main() baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/tests/test_utils.py000066400000000000000000000032601445677601600244070ustar00rootroot00000000000000import sys import unittest from unittest.mock import Mock, patch import numpy as np from baycomp.utils import check_args, call_shortcut, seaborn_plt class UtilsTest(unittest.TestCase): def test_check_args(self): y = x = np.ones((5, )) check_args(x, y) check_args(x, y, rope=10) check_args(x, y, rope=0) check_args(x, y, prior=11) check_args(x, y, prior=0) check_args(x, y, nsamples=1) check_args(x, y, nsamples=1.0) self.assertRaises(ValueError, check_args, np.ones((5, 1)), y) self.assertRaises(ValueError, check_args, x, np.ones((5, 1))) self.assertRaises(ValueError, check_args, x, np.ones((6, ))) self.assertRaises(ValueError, check_args, x, y, rope=-1) self.assertRaises(ValueError, check_args, x, y, prior=-1) self.assertRaises(ValueError, check_args, x, y, nsamples=-1) self.assertRaises(ValueError, check_args, x, y, nsamples=3.14) def test_call_shortcut(self): y = x = np.ones((5, )) rope = 1 names = ("a", "b") probs = (0.1, 0.2, 0.7) fig = object() sample = Mock(**{'probs.return_value': probs, 'plot.return_value': (fig, names)}) test = Mock(return_value=sample) self.assertEqual( call_shortcut(test, x, y, rope, 42, names=names, foo=13), probs) test.assert_called_with(x, y, rope, 42, foo=13) self.assertEqual( call_shortcut(test, x, y, rope, 42, names=names, foo=13, plot=True), (probs, (fig, names))) test.assert_called_with(x, y, rope, 42, foo=13) if __name__ == "__main__": unittest.main() baycomp-b5ed8c0cedb7968dd2ac6a9142c2a9667b7d1b5c/tests/utils.py000066400000000000000000000017631445677601600233560ustar00rootroot00000000000000import unittest from unittest.mock import patch, Mock class TestTestBase(unittest.TestCase): def assert_forwards(self, class_, method_name, *args, new_args=None, new_kwargs=None, meth_args=None, meth_kwargs=None, **kwargs): def default(x, value): return x if x is not None else value new_args = default(new_args, args) new_kwargs = default(new_kwargs, kwargs) meth_args = default(meth_args, ()) meth_kwargs = default(meth_kwargs, {}) mockres = object() mockmethod = Mock(return_value=mockres) with patch.object( class_, "__new__", return_value=Mock(**{method_name: mockmethod})) as mocknew: method = getattr(class_, method_name) self.assertEqual(method(*args, **kwargs), mockres) mocknew.assert_called_with(class_, *new_args, **new_kwargs) mockmethod.assert_called_with(*meth_args, **meth_kwargs)