Data
mfeat-fourier

mfeat-fourier

active 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.

77 features

binaryClass (target)nominal2 unique values
0 missing
att1numeric1990 unique values
0 missing
att2numeric1987 unique values
0 missing
att3numeric1991 unique values
0 missing
att4numeric1984 unique values
0 missing
att5numeric1990 unique values
0 missing
att6numeric1992 unique values
0 missing
att7numeric1990 unique values
0 missing
att8numeric1992 unique values
0 missing
att9numeric1992 unique values
0 missing
att10numeric1991 unique values
0 missing
att11numeric1985 unique values
0 missing
att12numeric1990 unique values
0 missing
att13numeric1989 unique values
0 missing
att14numeric1986 unique values
0 missing
att15numeric1988 unique values
0 missing
att16numeric1978 unique values
0 missing
att17numeric1984 unique values
0 missing
att18numeric1990 unique values
0 missing
att19numeric1989 unique values
0 missing
att20numeric1984 unique values
0 missing
att21numeric1983 unique values
0 missing
att22numeric1987 unique values
0 missing
att23numeric1984 unique values
0 missing
att24numeric1986 unique values
0 missing
att25numeric1979 unique values
0 missing
att26numeric1981 unique values
0 missing
att27numeric1983 unique values
0 missing
att28numeric1980 unique values
0 missing
att29numeric1984 unique values
0 missing
att30numeric1976 unique values
0 missing
att31numeric1982 unique values
0 missing
att32numeric1978 unique values
0 missing
att33numeric1983 unique values
0 missing
att34numeric1982 unique values
0 missing
att35numeric1978 unique values
0 missing
att36numeric1984 unique values
0 missing
att37numeric1984 unique values
0 missing
att38numeric1987 unique values
0 missing
att39numeric1974 unique values
0 missing
att40numeric1962 unique values
0 missing
att41numeric1979 unique values
0 missing
att42numeric1981 unique values
0 missing
att43numeric1978 unique values
0 missing
att44numeric1982 unique values
0 missing
att45numeric1976 unique values
0 missing
att46numeric1981 unique values
0 missing
att47numeric1979 unique values
0 missing
att48numeric1978 unique values
0 missing
att49numeric1987 unique values
0 missing
att50numeric1975 unique values
0 missing
att51numeric1984 unique values
0 missing
att52numeric1983 unique values
0 missing
att53numeric1983 unique values
0 missing
att54numeric1981 unique values
0 missing
att55numeric1979 unique values
0 missing
att56numeric1983 unique values
0 missing
att57numeric1984 unique values
0 missing
att58numeric1984 unique values
0 missing
att59numeric1984 unique values
0 missing
att60numeric1986 unique values
0 missing
att61numeric1981 unique values
0 missing
att62numeric1976 unique values
0 missing
att63numeric1982 unique values
0 missing
att64numeric1980 unique values
0 missing
att65numeric1984 unique values
0 missing
att66numeric1985 unique values
0 missing
att67numeric1985 unique values
0 missing
att68numeric1986 unique values
0 missing
att69numeric1989 unique values
0 missing
att70numeric1988 unique values
0 missing
att71numeric1988 unique values
0 missing
att72numeric1985 unique values
0 missing
att73numeric1987 unique values
0 missing
att74numeric1988 unique values
0 missing
att75numeric1986 unique values
0 missing
att76numeric1985 unique values
0 missing

107 properties

2000
Number of instances (rows) of the dataset.
77
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.
76
Number of numeric attributes.
1
Number of nominal attributes.
0.07
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.03
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.99
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.55
Second quartile (Median) of skewness among attributes of the numeric type.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.38
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
1.3
Percentage of binary attributes.
0.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.12
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.01
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.
2
The maximum number of distinct values among attributes of the nominal type.
0.04
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.44
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.03
Maximum skewness among attributes of the numeric type.
10
Percentage of instances belonging to the least frequent class.
98.7
Percentage of numeric attributes.
0.16
Third quartile of means among attributes of the numeric type.
0.97
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.18
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
200
Number of instances belonging to the least frequent class.
1.3
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.01
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.12
Mean kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.71
Third quartile of skewness among attributes of the numeric type.
0.94
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
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.13
Mean of means among attributes of the numeric type.
0.01
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.18
First quartile of kurtosis among attributes of the numeric type.
0.09
Third quartile of standard deviation of attributes of the numeric type.
0.97
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.09
First quartile of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
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.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
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
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.98
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.
0.41
First quartile of skewness among attributes of the numeric type.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
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.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.54
Mean skewness among attributes of the numeric type.
0.04
First quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.01
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.07
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.94
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
90
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.06
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.47
Entropy of the target attribute values.
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1800
Number of instances belonging to the most frequent class.
-1.13
Minimum kurtosis among attributes of the numeric type.
0.11
Second quartile (Median) of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.

7 tasks

553 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
205 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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