Data
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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0 missing
f497numeric3330 unique values
0 missing
f242numeric871 unique values
0 missing
f498numeric3379 unique values
0 missing
f243numeric845 unique values
0 missing
f499numeric3461 unique values
0 missing
f244numeric802 unique values
0 missing
f500numeric3509 unique values
0 missing
f245numeric787 unique values
0 missing
f501numeric3559 unique values
0 missing
f246numeric788 unique values
0 missing
f502numeric3564 unique values
0 missing
f247numeric798 unique values
0 missing
f503numeric3672 unique values
0 missing
f248numeric806 unique values
0 missing
f504numeric3769 unique values
0 missing
f249numeric808 unique values
0 missing
f505numeric3840 unique values
0 missing
f250numeric817 unique values
0 missing
f506numeric3862 unique values
0 missing
f251numeric847 unique values
0 missing
f507numeric3816 unique values
0 missing
f252numeric862 unique values
0 missing
f508numeric3826 unique values
0 missing
f253numeric861 unique values
0 missing
f509numeric3840 unique values
0 missing
f254numeric900 unique values
0 missing
f510numeric3885 unique values
0 missing
f255numeric917 unique values
0 missing
f511numeric3811 unique values
0 missing
f256numeric958 unique values
0 missing
f512numeric3811 unique values
0 missing

107 properties

7797
Number of instances (rows) of the dataset.
618
Number of attributes (columns) of the dataset.
26
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
617
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
26
Average number of distinct values among the attributes of the nominal type.
-0.58
First quartile of skewness among attributes of the numeric type.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.44
Mean skewness among attributes of the numeric type.
0.34
First quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.41
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
3.85
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.33
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
4.7
Entropy of the target attribute values.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
300
Number of instances belonging to the most frequent class.
-1.47
Minimum kurtosis among attributes of the numeric type.
0.2
Second quartile (Median) of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.99
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.92
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
136.54
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0.09
Second quartile (Median) of skewness among attributes of the numeric type.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.81
Maximum of means among attributes of the numeric type.
26
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.41
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.08
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-2.37
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
26
The maximum number of distinct values among attributes of the nominal type.
0.05
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.37
Third quartile of kurtosis among attributes of the numeric type.
0.3
Average class difference between consecutive instances.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.77
Maximum skewness among attributes of the numeric type.
3.82
Percentage of instances belonging to the least frequent class.
99.84
Percentage of numeric attributes.
0.42
Third quartile of means among attributes of the numeric type.
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
Maximum standard deviation of attributes of the numeric type.
298
Number of instances belonging to the least frequent class.
0.16
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.51
Third quartile of skewness among attributes of the numeric type.
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
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.61
Mean kurtosis among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.74
First quartile of kurtosis among attributes of the numeric type.
0.49
Third quartile of standard deviation of attributes of the numeric type.
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.06
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.14
First quartile of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

119 tasks

9919 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
105 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
39 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1322 runs - target_feature: class
1317 runs - target_feature: class
1316 runs - target_feature: class
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1299 runs - target_feature: class
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0 runs - target_feature: class
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0 runs - target_feature: class
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0 runs - target_feature: class
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