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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.

18 features

binaryClass (target)nominal2 unique values
0 missing
animal (ignore)nominal100 unique values
0 missing
hairnominal2 unique values
0 missing
feathersnominal2 unique values
0 missing
eggsnominal2 unique values
0 missing
milknominal2 unique values
0 missing
airbornenominal2 unique values
0 missing
aquaticnominal2 unique values
0 missing
predatornominal2 unique values
0 missing
toothednominal2 unique values
0 missing
backbonenominal2 unique values
0 missing
breathesnominal2 unique values
0 missing
venomousnominal2 unique values
0 missing
finsnominal2 unique values
0 missing
legsnumeric6 unique values
0 missing
tailnominal2 unique values
0 missing
domesticnominal2 unique values
0 missing
catsizenominal2 unique values
0 missing

107 properties

101
Number of instances (rows) of the dataset.
18
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.
1
Number of numeric attributes.
17
Number of nominal attributes.
0.65
Average class difference between consecutive instances.
0.98
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
0.14
Maximum skewness among attributes of the numeric type.
2.03
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.6
Third quartile of kurtosis 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
0.99
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
2.03
Maximum standard deviation of attributes of the numeric type.
40.59
Percentage of instances belonging to the least frequent class.
5.56
Percentage of numeric attributes.
2.84
Third quartile of means among attributes of the numeric type.
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
0.01
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
0.81
Average entropy of the attributes.
41
Number of instances belonging to the least frequent class.
94.44
Percentage of nominal attributes.
0.35
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.98
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
-0.6
Mean kurtosis among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.68
First quartile of entropy among attributes.
0.14
Third quartile of skewness among attributes of the numeric type.
2.03
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
0.99
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
2.84
Mean of means among attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.6
First quartile of kurtosis among attributes of the numeric type.
0.98
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
0.01
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
0.26
Average mutual information between the nominal attributes and the target attribute.
1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.84
First quartile of means among attributes of the numeric type.
0.02
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
0.98
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
2.14
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
16
Number of binary attributes.
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
0.96
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
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
2
Average number of distinct values among the attributes of the nominal type.
0.14
First quartile of skewness among attributes of the numeric type.
0.98
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
1
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
0.14
Mean skewness among attributes of the numeric type.
2.03
First quartile of standard deviation of attributes of the numeric type.
0.02
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
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
59.41
Percentage of instances belonging to the most frequent class.
2.03
Mean standard deviation of attributes of the numeric type.
0.82
Second quartile (Median) of entropy among attributes.
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Entropy of the target attribute values.
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
60
Number of instances belonging to the most frequent class.
0.4
Minimal entropy among attributes.
-0.6
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.99
Maximum entropy among attributes.
-0.6
Minimum kurtosis among attributes of the numeric type.
2.84
Second quartile (Median) of means among attributes of the numeric type.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.6
Maximum kurtosis among attributes of the numeric type.
2.84
Minimum of means among attributes of the numeric type.
0.15
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
2.84
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.14
Second quartile (Median) of skewness among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.18
Number of attributes divided by the number of instances.
0.97
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
88.89
Percentage of binary attributes.
2.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
3.77
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.
0.14
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.98
Third quartile of entropy among attributes.

6 tasks

175 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
1 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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