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kr-vs-k

kr-vs-k

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael Gomes Mantovani
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  • Chemistry Life Science study_144 study_7 study_76
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Author: Source: KEEL Please cite: Abstract: A chess endgame data set representing the positions on the board of the white king, the white rook, and the black king. The task is to determine the optimum number of turn required for white to win the game, which can be a draw if it takes more than sixteen turns. Attributes Details: 1. White_king_col {a, b, c, d, e, f, g, h} 2. White_king_row {1, 2, 3, 4, 5, 6, 7, 8} 3. White_rook_col {a, b, c, d, e, f, g, h} 4. White_rook_row {1, 2, 3, 4, 5, 6, 7, 8} 5. Black_king_col {a, b, c, d, e, f, g, h} 6. Black_king_row {1, 2, 3, 4, 5, 6, 7, 8} 7. Class - Game {draw, zero, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen}

7 features

Class (target)nominal18 unique values
0 missing
V1nominal4 unique values
0 missing
V2numeric4 unique values
0 missing
V3nominal8 unique values
0 missing
V4numeric8 unique values
0 missing
V5nominal8 unique values
0 missing
V6numeric8 unique values
0 missing

107 properties

28056
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
18
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.
3
Number of numeric attributes.
4
Number of nominal attributes.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
4.51
Maximum of means among attributes of the numeric type.
0.05
Minimal mutual information between the nominal attributes and the target attribute.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.19
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
2.25
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
26.25
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
18
The maximum number of distinct values among attributes of the nominal type.
-0.01
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
3
Third quartile of entropy among attributes.
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.76
Maximum skewness among attributes of the numeric type.
0.93
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.48
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.41
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.28
Maximum standard deviation of attributes of the numeric type.
0.1
Percentage of instances belonging to the least frequent class.
42.86
Percentage of numeric attributes.
4.51
Third quartile of means among attributes of the numeric type.
0.86
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
0.43
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.57
Average entropy of the attributes.
27
Number of instances belonging to the least frequent class.
57.14
Percentage of nominal attributes.
0.19
Third quartile of mutual information between the nominal attributes and the target attribute.
0.41
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
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.96
Mean kurtosis among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.76
First quartile of entropy among attributes.
0.76
Third quartile of skewness among attributes of the numeric type.
0.55
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.41
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.61
Mean of means among attributes of the numeric type.
0.66
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.23
First quartile of kurtosis among attributes of the numeric type.
2.28
Third quartile of standard deviation of attributes of the numeric type.
0.86
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
0.43
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.13
Average mutual information between the nominal attributes and the target attribute.
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.85
First quartile of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.41
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
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
18.26
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
0.05
First quartile of mutual information between the nominal attributes and the target attribute.
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.55
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
0.86
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
5.97
Standard deviation of the number of distinct values among attributes of the nominal type.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
9.5
Average number of distinct values among the attributes of the nominal type.
-0.01
First quartile of skewness among attributes of the numeric type.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.41
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.25
Mean skewness among attributes of the numeric type.
0.93
First quartile of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.55
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
0.45
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
16.23
Percentage of instances belonging to the most frequent class.
1.82
Mean standard deviation of attributes of the numeric type.
2.95
Second quartile (Median) of entropy among attributes.
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.5
Entropy of the target attribute values.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
4553
Number of instances belonging to the most frequent class.
1.76
Minimal entropy among attributes.
-1.18
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3
Maximum entropy among attributes.
-1.23
Minimum kurtosis among attributes of the numeric type.
4.45
Second quartile (Median) of means among attributes of the numeric type.
0.49
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.81
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.48
Maximum kurtosis among attributes of the numeric type.
1.85
Minimum of means among attributes of the numeric type.
0.17
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

14 tasks

25 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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