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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
reskrnominal2 unique values
0 missing
reskdnominal2 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
bxqsqnominal2 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
bkblknominal2 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
Show all 37 features

107 properties

NumberOfInstances
3196
Number of instances (rows) of the dataset.
NumberOfFeatures
37
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
0
Number of numeric attributes.
NumberOfSymbolicFeatures
37
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
1
Entropy of the target attribute values.
DecisionStumpAUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
52.14
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
52.22
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1669
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.2
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.59
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.02
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
29.81
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.03
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
47.78
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1527
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
35
Number of binary attributes.
PercentageOfBinaryFeatures
94.59
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.29
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.68
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.91
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0.16
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

12 tasks

Supervised Classification on kr-vs-kp
66 runs - estimation_procedure: 10% Holdout set - target_feature: class
Supervised Classification on kr-vs-kp
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on kr-vs-kp
0 runs - estimation_procedure: Leave one out - target_feature: class
Supervised Classification on kr-vs-kp
0 runs - estimation_procedure: Test on Training Data - target_feature: class
Supervised Classification on kr-vs-kp
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on kr-vs-kp
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on kr-vs-kp
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
Supervised Classification on kr-vs-kp
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Learning Curve on kr-vs-kp
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on kr-vs-kp
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
Learning Curve on kr-vs-kp
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: reskd
Supervised Data Stream Classification on kr-vs-kp
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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