Data
jungle_chess_2pcs_raw_endgame_complete

jungle_chess_2pcs_raw_endgame_complete

active ARFF Publicly available Visibility: public Uploaded 20-02-2018 by Jan van Rijn
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  • DouShouQi Games JungleChess OpenML-CC18 Retrogade study_135 study_99
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### Description ### This dataset is part of a collection datasets based on the game "Jungle Chess" (a.k.a. Dou Shou Qi). For a description of the rules, please refer to the paper (link attached). The paper also contains a description of various constructed features. As the tablebases are a disjoint set of several tablebases based on which (two) pieces are on the board, we have uploaded all tablebases that have explicit different content: * Rat vs Rat * Rat vs Panther * Rat vs. Lion * Rat vs. Elephant * Panther vs. Lion * Panther vs. Elephant * Tiger vs. Lion * Lion vs. Lion * Lion vs. Elephant * Elephant vs. Elephant * Complete (Combination of the above) * RAW Complete (Combination of the above, containing for both pieces just the rank, file and strength information). This dataset contains a similar classification problem as, e.g., the King and Rook vs. King problem and is suitable for classification tasks. (Note that this dataset is one of the above mentioned datasets). Additionally, note that several subproblems are very similar. Having seen a given positions from one of the tablebases arguably gives a lot of information about the outcome of the same position in the other tablebases. ### Please cite ### J. N. van Rijn and J. K. Vis, Endgame Analysis of Dou Shou Qi. ICGA Journal 37:2, 120--124, 2014. ArXiv link: https://arxiv.org/abs/1604.07312

7 features

class (target)nominal3 unique values
0 missing
white_piece0_strengthnumeric5 unique values
0 missing
white_piece0_filenumeric7 unique values
0 missing
white_piece0_ranknumeric9 unique values
0 missing
black_piece0_strengthnumeric5 unique values
0 missing
black_piece0_filenumeric7 unique values
0 missing
black_piece0_ranknumeric9 unique values
0 missing

62 properties

44819
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
3
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.
6
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
2.71
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.42
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-1.25
Maximum kurtosis among attributes of the numeric type.
3
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-1.25
Third quartile of kurtosis among attributes of the numeric type.
4.16
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
85.71
Percentage of numeric attributes.
4.16
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
14.29
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
-0.63
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
0
Maximum skewness among attributes of the numeric type.
2.1
Minimum standard deviation of attributes of the numeric type.
-1.42
First quartile of kurtosis among attributes of the numeric type.
2.76
Third quartile of standard deviation of attributes of the numeric type.
2.76
Maximum standard deviation of attributes of the numeric type.
9.67
Percentage of instances belonging to the least frequent class.
3
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
4335
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
-1.33
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
-0.63
First quartile of skewness among attributes of the numeric type.
3.72
Mean of means among attributes of the numeric type.
2.1
First quartile of standard deviation of attributes of the numeric type.
0.7
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
1.35
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.33
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
3
Average number of distinct values among the attributes of the nominal type.
4
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-0.21
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
51.46
Percentage of instances belonging to the most frequent class.
2.52
Mean standard deviation of attributes of the numeric type.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
23062
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

6 tasks

3743 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: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
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