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isolet

isolet

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Author: Ron Cole and Mark Fanty (cole@cse.ogi.edu, fanty@cse.ogi.edu) Donor: Tom Dietterich (tgd@cs.orst.edu) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/ISOLET) Please cite: UCI ### Description ISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjects spoke the name of each letter of the alphabet twice. Hence, there are 52 training examples from each speaker. The speakers are grouped into sets of 30 speakers each, 4 groups can serve as training set, the last group as the test set. A total of 3 examples are missing, the authors dropped them due to difficulties in recording. This is a good domain for a noisy, perceptual task. It is also a very good domain for testing the scaling abilities of algorithms. For example, C4.5 on this domain is slower than backpropagation! ### Source * Creators: Ron Cole and Mark Fanty Department of Computer Science and Engineering, Oregon Graduate Institute, Beaverton, OR 97006. cole '@' cse.ogi.edu, fanty '@' cse.ogi.edu * Donor: Tom Dietterich Department of Computer Science Oregon State University, Corvallis, OR 97331 tgd '@' cs.orst.edu ### Attributes Information All attributes are continuous, real-valued attributes scaled into the range -1.0 to 1.0. The features are described in the paper by Cole and Fanty cited below. The features include spectral coefficients; contour features, sonorant features, pre-sonorant features, and post-sonorant features. The exact order of appearance of the features is not known. ### Relevant papers Fanty, M., Cole, R. (1991). Spoken letter recognition. In Lippman, R. P., Moody, J., and Touretzky, D. S. (Eds). Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann. Dietterich, T. G., Bakiri, G. (1991) Error-correcting output codes: A general method for improving multiclass inductive learning programs. Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Anaheim, CA: AAAI Press. Dietterich, T. G., Bakiri, G. (1994) Solving Multiclass Learning Problems via Error-Correcting Output Codes.

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f586numeric3572 unique values
0 missing
f587numeric3542 unique values
0 missing
f588numeric3429 unique values
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f589numeric3431 unique values
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f592numeric3314 unique values
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f593numeric3307 unique values
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f594numeric3188 unique values
0 missing
f595numeric3174 unique values
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f596numeric3047 unique values
0 missing
f597numeric3110 unique values
0 missing
f598numeric2980 unique values
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0 missing
f600numeric3090 unique values
0 missing
f601numeric3107 unique values
0 missing
f602numeric3146 unique values
0 missing
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0 missing
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0 missing
f605numeric3223 unique values
0 missing
f606numeric3248 unique values
0 missing
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0 missing
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0 missing
f610numeric3333 unique values
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0 missing
Show first 100 features

107 properties

NumberOfInstances
7797
Number of instances (rows) of the dataset.
NumberOfFeatures
618
Number of attributes (columns) of the dataset.
NumberOfClasses
26
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
617
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.3
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
4.7
Entropy of the target attribute values.
DecisionStumpAUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.92
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.08
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
3.85
Percentage of instances belonging to the most frequent class.
MajorityClassSize
300
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
136.54
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.81
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
26
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
10.77
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.94
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
3.61
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.06
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
26
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.44
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.41
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.47
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.99
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
26
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-2.37
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.05
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
3.82
Percentage of instances belonging to the least frequent class.
MinorityClassSize
298
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.84
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.16
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.74
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0.14
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.58
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.34
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.33
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.2
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.09
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.41
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.37
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.42
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.51
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.49
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

134 tasks

Supervised Classification on isolet
16636 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on isolet
105 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on isolet
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on isolet
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on isolet
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on isolet
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on isolet
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Supervised Regression on isolet
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on isolet
39 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on isolet
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on isolet
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on isolet
0 runs - target_feature: class
Clustering on isolet
0 runs
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Clustering on isolet
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on isolet
1322 runs - target_feature: class
Subgroup Discovery on isolet
1317 runs - target_feature: class
Subgroup Discovery on isolet
1316 runs - target_feature: class
Subgroup Discovery on isolet
1316 runs - target_feature: class
Subgroup Discovery on isolet
1315 runs - target_feature: class
Subgroup Discovery on isolet
1315 runs - target_feature: class
Subgroup Discovery on isolet
1314 runs - target_feature: class
Subgroup Discovery on isolet
1314 runs - target_feature: class
Subgroup Discovery on isolet
1314 runs - target_feature: class
Subgroup Discovery on isolet
1311 runs - target_feature: class
Subgroup Discovery on isolet
1311 runs - target_feature: class
Subgroup Discovery on isolet
1311 runs - target_feature: class
Subgroup Discovery on isolet
1311 runs - target_feature: class
Subgroup Discovery on isolet
1311 runs - target_feature: class
Subgroup Discovery on isolet
1310 runs - target_feature: class
Subgroup Discovery on isolet
1309 runs - target_feature: class
Subgroup Discovery on isolet
1309 runs - target_feature: class
Subgroup Discovery on isolet
1309 runs - target_feature: class
Subgroup Discovery on isolet
1309 runs - target_feature: class
Subgroup Discovery on isolet
1308 runs - target_feature: class
Subgroup Discovery on isolet
1307 runs - target_feature: class
Subgroup Discovery on isolet
1306 runs - target_feature: class
Subgroup Discovery on isolet
1305 runs - target_feature: class
Subgroup Discovery on isolet
1304 runs - target_feature: class
Subgroup Discovery on isolet
1304 runs - target_feature: class
Subgroup Discovery on isolet
1299 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Subgroup Discovery on isolet
0 runs - target_feature: class
Define a new task

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