Data
Klaverjas2018

Klaverjas2018

active ARFF Publicly available Visibility: public Uploaded 07-11-2018 by Jan van Rijn
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Authors: J.N. van Rijn, F.W. Takes, J.K. Vis Please cite: Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas, [in Proceedings of BNAIC 2018](https://bnaic2018.nl/wp-content/uploads/2018/11/bnaic2018-proceedings.pdf#section*.46). Klaverjas is an example of the Jack-Nine card games, which are characterized as trick-taking games where the the Jack and nine of the trump suit are the highest-ranking trumps, and the tens and aces of other suits are the most valuable cards of these suits. It is played by four players in two teams. This dataset contains the game-theoretic value of almost a million configurations, given perfect play by both teams. It is assumed that player 0 starts and that the Diamondsuit is trump. Each of the configurations comes from a different equivalence class. Although the game theoretic value (expressedin the score difference between two teams) constitutes a regression problem, in the attached publication we viewed this as a classification problem, where the goal is to predict whether the starting team will obtain more points than the other team. This is represented in the field `outcome`. The fields `card_{S,H,D,C}_{A,10,K,Q,J,9,8,7}` are the attributes for theclassification problem, defining which player has a given card (each player has exactly 8 cards). The fields `leaf_count` and `time_real` are meta-data as result from the $\alpha\beta$-search procedure, and should not be used as predictors. Generating this dataset took more than 2 CPU years, and countless human days for verification of the programs and the results.

33 features

outcome (target)nominal2 unique values
0 missing
index (row identifier)string981541 unique values
0 missing
card_S_Anumeric4 unique values
0 missing
card_S_10numeric4 unique values
0 missing
card_S_Knumeric4 unique values
0 missing
card_S_Qnumeric4 unique values
0 missing
card_S_Jnumeric4 unique values
0 missing
card_S_9numeric4 unique values
0 missing
card_S_8numeric4 unique values
0 missing
card_S_7numeric4 unique values
0 missing
card_H_Anumeric4 unique values
0 missing
card_H_10numeric4 unique values
0 missing
card_H_Knumeric4 unique values
0 missing
card_H_Qnumeric4 unique values
0 missing
card_H_Jnumeric4 unique values
0 missing
card_H_9numeric4 unique values
0 missing
card_H_8numeric4 unique values
0 missing
card_H_7numeric4 unique values
0 missing
card_D_Anumeric4 unique values
0 missing
card_D_10numeric4 unique values
0 missing
card_D_Knumeric4 unique values
0 missing
card_D_Qnumeric4 unique values
0 missing
card_D_Jnumeric4 unique values
0 missing
card_D_9numeric4 unique values
0 missing
card_D_8numeric4 unique values
0 missing
card_D_7numeric4 unique values
0 missing
card_C_Anumeric4 unique values
0 missing
card_C_10numeric4 unique values
0 missing
card_C_Knumeric4 unique values
0 missing
card_C_Qnumeric4 unique values
0 missing
card_C_Jnumeric4 unique values
0 missing
card_C_9numeric4 unique values
0 missing
card_C_8numeric4 unique values
0 missing
card_C_7numeric4 unique values
0 missing
leaf_count (ignore)numeric972268 unique values
0 missing
time_real (ignore)numeric4253 unique values
0 missing

62 properties

981541
Number of instances (rows) of the dataset.
33
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.
32
Number of numeric attributes.
1
Number of nominal attributes.
1.5
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
96.97
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0
Minimum skewness among attributes of the numeric type.
3.03
Percentage of nominal attributes.
0
Third quartile of skewness among attributes of the numeric type.
0
Maximum skewness among attributes of the numeric type.
1.12
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.12
Third quartile of standard deviation of attributes of the numeric type.
1.12
Maximum standard deviation of attributes of the numeric type.
46.17
Percentage of instances belonging to the least frequent class.
-1.36
First quartile of kurtosis 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.
453202
Number of instances belonging to the least frequent class.
1.5
First quartile of means among attributes of the numeric type.
-1.36
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1.5
Mean of means among attributes of the numeric type.
-0
First quartile of skewness among attributes of the numeric type.
0.56
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
1.12
First quartile of standard deviation of attributes of the numeric type.
1
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.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
-1.36
Second quartile (Median) of kurtosis 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
Mean skewness among attributes of the numeric type.
1.5
Second quartile (Median) of means among attributes of the numeric type.
53.83
Percentage of instances belonging to the most frequent class.
1.12
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
528339
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.36
Minimum kurtosis among attributes of the numeric type.
3.03
Percentage of binary attributes.
1.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
-1.36
Maximum kurtosis among attributes of the numeric type.
1.5
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
-1.36
Third quartile of kurtosis among attributes of the numeric type.
1.5
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

2 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: outcome
0 runs - estimation_procedure: 50 times Clustering
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