Data
autos

autos

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.

26 features

binaryClass (target)nominal2 unique values
0 missing
normalized-lossesnumeric51 unique values
41 missing
makenominal22 unique values
0 missing
fuel-typenominal2 unique values
0 missing
aspirationnominal2 unique values
0 missing
num-of-doorsnominal2 unique values
2 missing
body-stylenominal5 unique values
0 missing
drive-wheelsnominal3 unique values
0 missing
engine-locationnominal2 unique values
0 missing
wheel-basenumeric53 unique values
0 missing
lengthnumeric75 unique values
0 missing
widthnumeric44 unique values
0 missing
heightnumeric49 unique values
0 missing
curb-weightnumeric171 unique values
0 missing
engine-typenominal7 unique values
0 missing
num-of-cylindersnominal7 unique values
0 missing
engine-sizenumeric44 unique values
0 missing
fuel-systemnominal8 unique values
0 missing
borenumeric38 unique values
4 missing
strokenumeric36 unique values
4 missing
compression-rationumeric32 unique values
0 missing
horsepowernumeric59 unique values
2 missing
peak-rpmnumeric23 unique values
2 missing
city-mpgnumeric29 unique values
0 missing
highway-mpgnumeric30 unique values
0 missing
pricenumeric186 unique values
4 missing

107 properties

205
Number of instances (rows) of the dataset.
26
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
59
Number of missing values in the dataset.
46
Number of instances with at least one value missing.
15
Number of numeric attributes.
11
Number of nominal attributes.
67.32
Percentage of instances belonging to the most frequent class.
606.99
Mean standard deviation of attributes of the numeric type.
1.19
Second quartile (Median) of entropy among attributes.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.67
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
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
138
Number of instances belonging to the most frequent class.
0.11
Minimal entropy among attributes.
0.58
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.91
Entropy of the target attribute values.
4.12
Maximum entropy among attributes.
-0.83
Minimum kurtosis among attributes of the numeric type.
98.76
Second quartile (Median) of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
5.31
Maximum kurtosis among attributes of the numeric type.
3.26
Minimum of means among attributes of the numeric type.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
13207.13
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.33
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
19.23
Percentage of binary attributes.
6.89
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.13
Number of attributes divided by the number of instances.
22
The maximum number of distinct values among attributes of the nominal type.
-0.68
Minimum skewness among attributes of the numeric type.
22.44
Percentage of instances having missing values.
1.8
Third quartile of entropy among attributes.
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
10.19
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.61
Maximum skewness among attributes of the numeric type.
0.27
Minimum standard deviation of attributes of the numeric type.
1.11
Percentage of missing values.
2.62
Third quartile of kurtosis among attributes of the numeric type.
0.82
Average class difference between consecutive instances.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
7947.07
Maximum standard deviation of attributes of the numeric type.
32.68
Percentage of instances belonging to the least frequent class.
57.69
Percentage of numeric attributes.
174.05
Third quartile of means among attributes of the numeric type.
0.89
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.39
Average entropy of the attributes.
67
Number of instances belonging to the least frequent class.
42.31
Percentage of nominal attributes.
0.14
Third quartile of mutual information between the nominal attributes and the target attribute.
0.15
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
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.36
Mean kurtosis among attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.63
First quartile of entropy among attributes.
1.39
Third quartile of skewness among attributes of the numeric type.
0.67
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
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1447.09
Mean of means among attributes of the numeric type.
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.04
First quartile of kurtosis among attributes of the numeric type.
41.64
Third quartile of standard deviation of attributes of the numeric type.
0.89
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.09
Average mutual information between the nominal attributes and the target attribute.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
25.22
First quartile of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
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
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
14.57
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
5
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.67
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
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.64
Average number of distinct values among the attributes of the nominal type.
0.07
First quartile of skewness among attributes of the numeric type.
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.89
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
5.92
Standard deviation of the number of distinct values among attributes of the nominal type.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.8
Mean skewness among attributes of the numeric type.
2.44
First quartile of standard deviation of attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.15
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

7 tasks

509 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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