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kr-vs-kp

kr-vs-kp

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Author: Source: Unknown - Please cite: 1. Title: Chess End-Game -- King+Rook versus King+Pawn on a7 (usually abbreviated KRKPA7). The pawn on a7 means it is one square away from queening. It is the King+Rook's side (white) to move. 2. Sources: (a) Database originally generated and described by Alen Shapiro. (b) Donor/Coder: Rob Holte (holte@uottawa.bitnet). The database was supplied to Holte by Peter Clark of the Turing Institute in Glasgow (pete@turing.ac.uk). (c) Date: 1 August 1989 3. Past Usage: - Alen D. Shapiro (1983,1987), "Structured Induction in Expert Systems", Addison-Wesley. This book is based on Shapiro's Ph.D. thesis (1983) at the University of Edinburgh entitled "The Role of Structured Induction in Expert Systems". - Stephen Muggleton (1987), "Structuring Knowledge by Asking Questions", pp.218-229 in "Progress in Machine Learning", edited by I. Bratko and Nada Lavrac, Sigma Press, Wilmslow, England SK9 5BB. - Robert C. Holte, Liane Acker, and Bruce W. Porter (1989), "Concept Learning and the Problem of Small Disjuncts", Proceedings of IJCAI. Also available as technical report AI89-106, Computer Sciences Department, University of Texas at Austin, Austin, Texas 78712. 4. Relevant Information: The dataset format is described below. Note: the format of this database was modified on 2/26/90 to conform with the format of all the other databases in the UCI repository of machine learning databases. 5. Number of Instances: 3196 total 6. Number of Attributes: 36 7. Attribute Summaries: Classes (2): -- White-can-win ("won") and White-cannot-win ("nowin"). I believe that White is deemed to be unable to win if the Black pawn can safely advance. Attributes: see Shapiro's book. 8. Missing Attributes: -- none 9. Class Distribution: In 1669 of the positions (52%), White can win. In 1527 of the positions (48%), White cannot win. The format for instances in this database is a sequence of 37 attribute values. Each instance is a board-descriptions for this chess endgame. The first 36 attributes describe the board. The last (37th) attribute is the classification: "win" or "nowin". There are 0 missing values. A typical board-description is f,f,f,f,f,f,f,f,f,f,f,f,l,f,n,f,f,t,f,f,f,f,f,f,f,t,f,f,f,f,f,f,f,t,t,n,won The names of the features do not appear in the board-descriptions. Instead, each feature correponds to a particular position in the feature-value list. For example, the head of this list is the value for the feature "bkblk". The following is the list of features, in the order in which their values appear in the feature-value list: [bkblk,bknwy,bkon8,bkona,bkspr,bkxbq,bkxcr,bkxwp,blxwp,bxqsq,cntxt,dsopp,dwipd, hdchk,katri,mulch,qxmsq,r2ar8,reskd,reskr,rimmx,rkxwp,rxmsq,simpl,skach,skewr, skrxp,spcop,stlmt,thrsk,wkcti,wkna8,wknck,wkovl,wkpos,wtoeg] In the file, there is one instance (board position) per line. Num Instances: 3196 Num Attributes: 37 Num Continuous: 0 (Int 0 / Real 0) Num Discrete: 37 Missing values: 0 / 0.0% name type enum ints real missing distinct (1) 1 'bkblk' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 2 'bknwy' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 3 'bkon8' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 4 'bkona' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 5 'bkspr' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 6 'bkxbq' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 7 'bkxcr' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 8 'bkxwp' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 9 'blxwp' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 10 'bxqsq' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 11 'cntxt' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 12 'dsopp' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 13 'dwipd' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 14 'hdchk' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 15 'katri' Enum 100% 0% 0% 0 / 0% 3 / 0% 0% 16 'mulch' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 17 'qxmsq' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 18 'r2ar8' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 19 'reskd' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 20 'reskr' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 21 'rimmx' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 22 'rkxwp' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 23 'rxmsq' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 24 'simpl' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 25 'skach' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 26 'skewr' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 27 'skrxp' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 28 'spcop' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 29 'stlmt' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 30 'thrsk' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 31 'wkcti' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 32 'wkna8' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 33 'wknck' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 34 'wkovl' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 35 'wkpos' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 36 'wtoeg' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 37 'class' Enum 100% 0% 0% 0 / 0% 2 / 0% 0%

37 features

class (target)nominal2 unique values
0 missing
bkblknominal2 unique values
0 missing
bknwynominal2 unique values
0 missing
bkon8nominal2 unique values
0 missing
bkonanominal2 unique values
0 missing
bksprnominal2 unique values
0 missing
bkxbqnominal2 unique values
0 missing
bkxcrnominal2 unique values
0 missing
bkxwpnominal2 unique values
0 missing
blxwpnominal2 unique values
0 missing
bxqsqnominal2 unique values
0 missing
cntxtnominal2 unique values
0 missing
dsoppnominal2 unique values
0 missing
dwipdnominal2 unique values
0 missing
hdchknominal2 unique values
0 missing
katrinominal3 unique values
0 missing
mulchnominal2 unique values
0 missing
qxmsqnominal2 unique values
0 missing
r2ar8nominal2 unique values
0 missing
reskdnominal2 unique values
0 missing
reskrnominal2 unique values
0 missing
rimmxnominal2 unique values
0 missing
rkxwpnominal2 unique values
0 missing
rxmsqnominal2 unique values
0 missing
simplnominal2 unique values
0 missing
skachnominal2 unique values
0 missing
skewrnominal2 unique values
0 missing
skrxpnominal2 unique values
0 missing
spcopnominal2 unique values
0 missing
stlmtnominal2 unique values
0 missing
thrsknominal2 unique values
0 missing
wkctinominal2 unique values
0 missing
wkna8nominal2 unique values
0 missing
wkncknominal2 unique values
0 missing
wkovlnominal2 unique values
0 missing
wkposnominal2 unique values
0 missing
wtoegnominal2 unique values
0 missing

107 properties

3196
Number of instances (rows) of the dataset.
37
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.
0
Number of numeric attributes.
37
Number of nominal attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
94.59
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
0.2
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
0.91
Third quartile of entropy among attributes.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
52.14
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.96
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
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
47.78
Percentage of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
0.06
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.59
Average entropy of the attributes.
1527
Number of instances belonging to the least frequent class.
0.29
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.88
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
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.96
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
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.12
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.06
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Average mutual information between the nominal attributes and the target attribute.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
35
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.88
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
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
29.81
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
First quartile of skewness among attributes of the numeric type.
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.96
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
0.16
Standard deviation of the number of distinct values among attributes of the nominal type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.03
Average number of distinct values among the attributes of the nominal type.
First quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.06
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
0.68
Second quartile (Median) of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.88
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
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
52.22
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1669
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.

13 tasks

50 runs - estimation_procedure: 10% Holdout set - target_feature: class
14 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: wkpos
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - 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: wtoeg
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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