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active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

14 features

binaryClass (target)nominal2 unique values
0 missing
agenumeric41 unique values
0 missing
sexnominal2 unique values
0 missing
cpnominal4 unique values
0 missing
trestbpsnumeric49 unique values
0 missing
cholnumeric152 unique values
0 missing
fbsnominal2 unique values
0 missing
restecgnominal3 unique values
0 missing
thalachnumeric91 unique values
0 missing
exangnominal2 unique values
0 missing
oldpeaknumeric40 unique values
0 missing
slopenominal3 unique values
0 missing
canumeric4 unique values
5 missing
thalnominal3 unique values
2 missing

107 properties

303
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
7
Number of missing values in the dataset.
7
Number of instances with at least one value missing.
6
Number of numeric attributes.
8
Number of nominal attributes.
0.54
Average class difference between consecutive instances.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.27
Maximum skewness among attributes of the numeric type.
0.94
Minimum standard deviation of attributes of the numeric type.
0.17
Percentage of missing values.
2.31
Third quartile of kurtosis among attributes of the numeric type.
0.78
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
51.83
Maximum standard deviation of attributes of the numeric type.
45.54
Percentage of instances belonging to the least frequent class.
42.86
Percentage of numeric attributes.
173.8
Third quartile of means among attributes of the numeric type.
0.24
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
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.11
Average entropy of the attributes.
138
Number of instances belonging to the least frequent class.
57.14
Percentage of nominal attributes.
0.2
Third quartile of mutual information between the nominal attributes and the target attribute.
0.52
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
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.11
Mean kurtosis among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.9
First quartile of entropy among attributes.
1.21
Third quartile of skewness among attributes of the numeric type.
30.14
Third quartile of standard deviation of attributes of the numeric type.
0.78
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
97.27
Mean of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.18
First quartile of kurtosis among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.24
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
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.11
Average mutual information between the nominal attributes and the target attribute.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.95
First quartile of means among attributes of the numeric type.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.52
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
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
9.27
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.78
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
0.74
Standard deviation of the number of distinct values among attributes of the nominal type.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.63
Average number of distinct values among the attributes of the nominal type.
-0.29
First quartile of skewness among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.24
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
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.6
Mean skewness among attributes of the numeric type.
1.11
First quartile of standard deviation of attributes of the numeric type.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.52
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
0.22
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
54.46
Percentage of instances belonging to the most frequent class.
17.24
Mean standard deviation of attributes of the numeric type.
1.09
Second quartile (Median) of entropy among attributes.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
165
Number of instances belonging to the most frequent class.
0.61
Minimal entropy among attributes.
0.59
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.74
Maximum entropy among attributes.
-0.54
Minimum kurtosis among attributes of the numeric type.
93
Second quartile (Median) of means among attributes of the numeric type.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4.51
Maximum kurtosis among attributes of the numeric type.
0.67
Minimum of means among attributes of the numeric type.
0.12
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
246.26
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.93
Second quartile (Median) of skewness among attributes of the numeric type.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.05
Number of attributes divided by the number of instances.
0.21
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
28.57
Percentage of binary attributes.
13.31
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
9.16
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
-0.54
Minimum skewness among attributes of the numeric type.
2.31
Percentage of instances having missing values.
1.29
Third quartile of entropy among attributes.

7 tasks

505 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
212 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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