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in_preparation 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.

35 features

binaryClass (target)nominal2 unique values
0 missing
Nnumeric2796 unique values
0 missing
TREEnominal57 unique values
0 missing
BRnominal10 unique values
12 missing
TLnumeric170 unique values
0 missing
INnumeric26 unique values
0 missing
INTERNODE_1numeric30 unique values
0 missing
INTERNODE_2numeric68 unique values
64 missing
INTERNODE_3numeric117 unique values
637 missing
INTERNODE_4numeric128 unique values
1634 missing
INTERNODE_5numeric113 unique values
1999 missing
INTERNODE_6numeric115 unique values
2172 missing
INTERNODE_7numeric99 unique values
2308 missing
INTERNODE_8numeric95 unique values
2406 missing
INTERNODE_9numeric86 unique values
2489 missing
INTERNODE_10numeric86 unique values
2543 missing
INTERNODE_11numeric85 unique values
2578 missing
INTERNODE_12numeric78 unique values
2608 missing
INTERNODE_13numeric76 unique values
2634 missing
INTERNODE_14numeric72 unique values
2653 missing
INTERNODE_15numeric66 unique values
2660 missing
INTERNODE_16numeric52 unique values
2678 missing
INTERNODE_17numeric51 unique values
2711 missing
INTERNODE_18numeric43 unique values
2725 missing
INTERNODE_19numeric35 unique values
2740 missing
INTERNODE_20numeric23 unique values
2753 missing
INTERNODE_21numeric18 unique values
2770 missing
INTERNODE_22numeric10 unique values
2779 missing
INTERNODE_23numeric9 unique values
2786 missing
INTERNODE_24numeric7 unique values
2789 missing
INTERNODE_25numeric3 unique values
2792 missing
INTERNODE_26numeric1 unique values
2795 missing
INTERNODE_27numeric1 unique values
2795 missing
INTERNODE_28numeric1 unique values
2795 missing
INTERNODE_29numeric1 unique values
2795 missing

107 properties

2796
Number of instances (rows) of the dataset.
35
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
68100
Number of missing values in the dataset.
2795
Number of instances with at least one value missing.
32
Number of numeric attributes.
3
Number of nominal attributes.
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
2116
Number of instances belonging to the most frequent class.
3.01
Minimal entropy among attributes.
-0.25
Second quartile (Median) of kurtosis among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.8
Entropy of the target attribute values.
5.58
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
3.01
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
88.43
Maximum kurtosis among attributes of the numeric type.
0.3
Minimum of means among attributes of the numeric type.
0.4
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1398.5
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
0.72
Second quartile (Median) of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.01
Number of attributes divided by the number of instances.
0.8
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
2.86
Percentage of binary attributes.
2.35
Second quartile (Median) of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1.98
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
57
The maximum number of distinct values among attributes of the nominal type.
0
Minimum skewness among attributes of the numeric type.
99.96
Percentage of instances having missing values.
5.58
Third quartile of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.53
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
69.59
Percentage of missing values.
3.16
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
807.28
Maximum standard deviation of attributes of the numeric type.
24.32
Percentage of instances belonging to the least frequent class.
91.43
Percentage of numeric attributes.
4.15
Third quartile of means among attributes of the numeric type.
1
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.29
Average entropy of the attributes.
680
Number of instances belonging to the least frequent class.
8.57
Percentage of nominal attributes.
0.8
Third quartile of mutual information between the nominal attributes and the target attribute.
0
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
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.19
Mean kurtosis among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.01
First quartile of entropy among attributes.
1.79
Third quartile of skewness among attributes of the numeric type.
1
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
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
46.43
Mean of means among attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.55
First quartile of kurtosis among attributes of the numeric type.
2.91
Third quartile of standard deviation of attributes of the numeric type.
1
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.4
Average mutual information between the nominal attributes and the target attribute.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.19
First quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
9.61
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.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
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
29.72
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
23
Average number of distinct values among the attributes of the nominal type.
0.4
First quartile of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
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
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.27
Mean skewness among attributes of the numeric type.
0.93
First quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
0.25
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
75.68
Percentage of instances belonging to the most frequent class.
27.7
Mean standard deviation of attributes of the numeric type.
4.29
Second quartile (Median) of entropy among attributes.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
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

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