Data
heart-h

heart-h

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael Gomes Mantovani
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Author: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. Source: [original](http://www.openml.org/d/51) - UCI Please cite: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. * Dataset: This is a reprocessed version of heart-h (hungarian), the heart disease reprocessed hungarian dataset from UCI.

14 features

Class (target)nominal5 unique values
0 missing
V1numeric38 unique values
0 missing
V2numeric2 unique values
0 missing
V3numeric4 unique values
0 missing
V4numeric32 unique values
0 missing
V5numeric154 unique values
0 missing
V6numeric3 unique values
0 missing
V7numeric4 unique values
0 missing
V8numeric72 unique values
0 missing
V9numeric3 unique values
0 missing
V10numeric10 unique values
0 missing
V11numeric4 unique values
0 missing
V12numeric3 unique values
0 missing
V13numeric4 unique values
0 missing

107 properties

294
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
5
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.
13
Number of numeric attributes.
1
Number of nominal attributes.
0.81
First quartile of standard deviation of attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.36
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.62
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.29
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
0.39
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
63.95
Percentage of instances belonging to the most frequent class.
12.61
Mean standard deviation of attributes of the numeric type.
3.05
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.64
Entropy of the target attribute values.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
188
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.59
Second quartile (Median) of means among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.62
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.36
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
111.2
Maximum kurtosis among attributes of the numeric type.
-8.85
Minimum of means among attributes of the numeric type.
-0.47
Second quartile (Median) of skewness among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
230.52
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
1.5
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.67
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.
Maximum mutual information between the nominal attributes and the target attribute.
5
The minimal number of distinct values among attributes of the nominal type.
-7.32
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.41
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.
5
The maximum number of distinct values among attributes of the nominal type.
0.45
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
63.79
Third quartile of kurtosis among attributes of the numeric type.
0.46
Average class difference between consecutive instances.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.09
Maximum skewness among attributes of the numeric type.
5.1
Percentage of instances belonging to the least frequent class.
92.86
Percentage of numeric attributes.
89.96
Third quartile of means among attributes of the numeric type.
0.73
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
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
95.41
Maximum standard deviation of attributes of the numeric type.
15
Number of instances belonging to the least frequent class.
7.14
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.36
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
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.08
Third quartile of skewness among attributes of the numeric type.
0.29
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
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
27.33
Mean kurtosis among attributes of the numeric type.
0.41
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.74
First quartile of kurtosis among attributes of the numeric type.
13.62
Third quartile of standard deviation of attributes of the numeric type.
0.73
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
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
40.93
Mean of means among attributes of the numeric type.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-2.66
First quartile of means among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.36
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
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.29
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
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-3.29
First quartile of skewness among attributes of the numeric type.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.73
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.
0.38
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
5
Average number of distinct values among the attributes of the nominal type.

13 tasks

107 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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