Data
cars

cars

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.

9 features

binaryClass (target)nominal2 unique values
0 missing
namenominal312 unique values
0 missing
mpgnumeric129 unique values
8 missing
cylindersnominal5 unique values
0 missing
displacementnumeric83 unique values
0 missing
horsepowernumeric93 unique values
6 missing
weightnumeric356 unique values
0 missing
accelerationnumeric96 unique values
0 missing
model.yearnumeric13 unique values
0 missing

107 properties

406
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
14
Number of missing values in the dataset.
14
Number of instances with at least one value missing.
6
Number of numeric attributes.
3
Number of nominal attributes.
290.44
Third quartile of standard deviation of attributes of the numeric type.
0.93
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
565.71
Mean of means among attributes of the numeric type.
0.21
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.92
First quartile of kurtosis among attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.11
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
0.2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.66
Average mutual information between the nominal attributes and the target attribute.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
21.52
First quartile of means among attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
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
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
6.33
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.
0.37
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.93
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
178.12
Standard deviation of the number of distinct values among attributes of the nominal type.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
106.33
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.49
Mean skewness among attributes of the numeric type.
3.51
First quartile of standard deviation of attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.77
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
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
62.56
Percentage of instances belonging to the most frequent class.
167.51
Mean standard deviation of attributes of the numeric type.
4.85
Second quartile (Median) of entropy among attributes.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
Entropy of the target attribute values.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
254
Number of instances belonging to the most frequent class.
1.59
Minimal entropy among attributes.
-0.66
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
8.11
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
90.5
Second quartile (Median) of means among attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.54
Maximum kurtosis among attributes of the numeric type.
15.52
Minimum of means among attributes of the numeric type.
0.66
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
2979.41
Maximum of means among attributes of the numeric type.
0.37
Minimal mutual information between the nominal attributes and the target attribute.
0.48
Second quartile (Median) of skewness among attributes of the numeric type.
23.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
0.95
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
11.11
Percentage of binary attributes.
8.11
Third quartile of entropy among attributes.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1.44
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
312
The maximum number of distinct values among attributes of the nominal type.
0.02
Minimum skewness among attributes of the numeric type.
3.45
Percentage of instances having missing values.
0.42
Third quartile of kurtosis among attributes of the numeric type.
0.71
Average class difference between consecutive instances.
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.88
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.
2.8
Minimum standard deviation of attributes of the numeric type.
0.38
Percentage of missing values.
890.94
Third quartile of means among attributes of the numeric type.
0.93
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
847
Maximum standard deviation of attributes of the numeric type.
37.44
Percentage of instances belonging to the least frequent class.
66.67
Percentage of numeric attributes.
0.95
Third quartile of mutual information between the nominal attributes and the target attribute.
0.11
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
0.2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.85
Average entropy of the attributes.
152
Number of instances belonging to the least frequent class.
33.33
Percentage of nominal attributes.
0.78
Third quartile of skewness among attributes of the numeric type.
0.77
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
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.4
Mean kurtosis among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.59
First quartile of entropy among attributes.

7 tasks

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