Data
echoMonths

echoMonths

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized mythbusting_1 study_1 study_123 study_15 study_20 study_41
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

10 features

binaryClass (target)nominal2 unique values
0 missing
still_alivenominal2 unique values
0 missing
agenumeric38 unique values
5 missing
pericardialnominal2 unique values
0 missing
fractionalnumeric51 unique values
7 missing
epssnumeric82 unique values
14 missing
lvddnumeric93 unique values
10 missing
wall_scorenumeric44 unique values
3 missing
wall_indexnumeric58 unique values
1 missing
alive_at_1nominal2 unique values
57 missing

107 properties

130
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
97
Number of missing values in the dataset.
69
Number of instances with at least one value missing.
6
Number of numeric attributes.
4
Number of nominal attributes.
15.97
Mean of means among attributes of the numeric type.
0.22
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.66
First quartile of kurtosis among attributes of the numeric type.
7.64
Third quartile of standard deviation of attributes of the numeric type.
0.77
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.19
Average mutual information between the nominal attributes and the target attribute.
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.09
First quartile of means among attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.2
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
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.06
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.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.6
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.18
First quartile of skewness among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
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.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.79
Mean skewness among attributes of the numeric type.
0.37
First quartile of standard deviation of attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.2
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.7
Mean standard deviation of attributes of the numeric type.
0.69
Second quartile (Median) of entropy among attributes.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.6
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
0.32
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.77
Percentage of instances belonging to the most frequent class.
0.67
Minimal entropy among attributes.
1.16
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
66
Number of instances belonging to the most frequent class.
0.91
Maximum entropy among attributes.
0.26
Minimum kurtosis among attributes of the numeric type.
8.48
Second quartile (Median) of means among attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4.12
Maximum kurtosis among attributes of the numeric type.
0.22
Minimum of means among attributes of the numeric type.
0.21
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.2
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
62.83
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
0.84
Second quartile (Median) of skewness among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.34
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
40
Percentage of binary attributes.
2.92
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.72
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.
2
The maximum number of distinct values among attributes of the nominal type.
0.17
Minimum skewness among attributes of the numeric type.
53.08
Percentage of instances having missing values.
0.91
Third quartile of entropy among attributes.
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
5.36
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.42
Maximum skewness among attributes of the numeric type.
0.11
Minimum standard deviation of attributes of the numeric type.
7.46
Percentage of missing values.
2.24
Third quartile of kurtosis among attributes of the numeric type.
0.53
Average class difference between consecutive instances.
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
8.37
Maximum standard deviation of attributes of the numeric type.
49.23
Percentage of instances belonging to the least frequent class.
60
Percentage of numeric attributes.
26.53
Third quartile of means among attributes of the numeric type.
0.77
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.76
Average entropy of the attributes.
64
Number of instances belonging to the least frequent class.
40
Percentage of nominal attributes.
0.34
Third quartile of mutual information between the nominal attributes and the target attribute.
0.2
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
0.31
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.52
Mean kurtosis among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.67
First quartile of entropy among attributes.
1.33
Third quartile of skewness among attributes of the numeric type.
0.6
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

6 tasks

519 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
225 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - 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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