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puma32H

puma32H

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized_regression_problem mythbusting_1 study_1 study_15 study_20 study_41 study_7
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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').

33 features

binaryClass (target)nominal2 unique values
0 missing
theta1numeric8188 unique values
0 missing
theta2numeric8186 unique values
0 missing
theta3numeric8186 unique values
0 missing
theta4numeric8185 unique values
0 missing
theta5numeric8178 unique values
0 missing
theta6numeric8180 unique values
0 missing
thetad1numeric8184 unique values
0 missing
thetad2numeric8187 unique values
0 missing
thetad3numeric8186 unique values
0 missing
thetad4numeric8183 unique values
0 missing
thetad5numeric8183 unique values
0 missing
thetad6numeric8188 unique values
0 missing
tau1numeric8192 unique values
0 missing
tau2numeric8192 unique values
0 missing
tau3numeric8192 unique values
0 missing
tau4numeric8192 unique values
0 missing
tau5numeric8192 unique values
0 missing
dm1numeric8181 unique values
0 missing
dm2numeric8180 unique values
0 missing
dm3numeric8178 unique values
0 missing
dm4numeric8187 unique values
0 missing
dm5numeric8173 unique values
0 missing
da1numeric8176 unique values
0 missing
da2numeric8173 unique values
0 missing
da3numeric8179 unique values
0 missing
da4numeric8179 unique values
0 missing
da5numeric8181 unique values
0 missing
db1numeric8180 unique values
0 missing
db2numeric8175 unique values
0 missing
db3numeric8176 unique values
0 missing
db4numeric8176 unique values
0 missing
db5numeric8173 unique values
0 missing

107 properties

8192
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
2
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.
32
Number of numeric attributes.
1
Number of nominal attributes.
Maximum entropy among attributes.
-1.23
Minimum kurtosis among attributes of the numeric type.
0.29
Second quartile (Median) of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
-1.16
Maximum kurtosis among attributes of the numeric type.
-0.53
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.13
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1.39
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
3.03
Percentage of binary attributes.
1.35
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.77
Area Under the ROC Curve 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.
2
The maximum number of distinct values among attributes of the nominal type.
-0.03
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.03
Maximum skewness among attributes of the numeric type.
0.65
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-1.19
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.13
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
43.75
Maximum standard deviation of attributes of the numeric type.
49.61
Percentage of instances belonging to the least frequent class.
96.97
Percentage of numeric attributes.
1.38
Third quartile of means among attributes of the numeric type.
0.65
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
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
4064
Number of instances belonging to the least frequent class.
3.03
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.34
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
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1.2
Mean kurtosis among attributes of the numeric type.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
0.31
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.13
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.66
Mean of means among attributes of the numeric type.
0.36
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.21
First quartile of kurtosis among attributes of the numeric type.
1.36
Third quartile of standard deviation of attributes of the numeric type.
0.65
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
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.34
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
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.87
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.13
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.31
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.13
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.01
First quartile of skewness among attributes of the numeric type.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.65
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0
Mean skewness among attributes of the numeric type.
0.65
First quartile of standard deviation of attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
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
0.44
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.39
Percentage of instances belonging to the most frequent class.
7.59
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.13
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.31
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
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
4128
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-1.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.

7 tasks

556 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
206 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - 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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