Data
poker-hand

poker-hand

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Author: Robert Cattral, Franz Oppacher Source: UCI Please cite: * Abstract: Purpose is to predict poker hands * Source - Creators: Robert Cattral (cattral '@' gmail.com) Franz Oppacher (oppacher '@' scs.carleton.ca) Carleton University, Department of Computer Science Intelligent Systems Research Unit 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6 * Data Set Information: Each record is an example of a hand consisting of five playing cards drawn from a standard deck of 52. Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the "Poker Hand". The order of cards is important, which is why there are 480 possible Royal Flush hands as compared to 4 (one for each suit). * Attribute Information: 1) S1 "Suit of card #1" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 2) C1 "Rank of card #1" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 3) S2 "Suit of card #2" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 4) C2 "Rank of card #2" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 5) S3 "Suit of card #3" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 6) C3 "Rank of card #3" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 7) S4 "Suit of card #4" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 8) C4 "Rank of card #4" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 9) S5 "Suit of card #5" Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs} 10) C5 "Rank of card 5" Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King) 11) CLASS "Poker Hand" Ordinal (0-9) 0: Nothing in hand; not a recognized poker hand 1: One pair; one pair of equal ranks within five cards 2: Two pairs; two pairs of equal ranks within five cards 3: Three of a kind; three equal ranks within five cards 4: Straight; five cards, sequentially ranked with no gaps 5: Flush; five cards with the same suit 6: Full house; pair + different rank three of a kind 7: Four of a kind; four equal ranks within five cards 8: Straight flush; straight + flush 9: Royal flush; {Ace, King, Queen, Jack, Ten} + flush * Relevant Papers: R. Cattral, F. Oppacher, D. Deugo. Evolutionary Data Mining with Automatic Rule Generalization. Recent Advances in Computers, Computing and Communications, pp.296-300, WSEAS Press, 2002. Note: This was a slightly different dataset that had more classes, and was considerably more difficult.

11 features

Class (target)nominal10 unique values
0 missing
V1numeric4 unique values
0 missing
V2numeric13 unique values
0 missing
V3numeric4 unique values
0 missing
V4numeric13 unique values
0 missing
V5numeric4 unique values
0 missing
V6numeric13 unique values
0 missing
V7numeric4 unique values
0 missing
V8numeric13 unique values
0 missing
V9numeric4 unique values
0 missing
V10numeric13 unique values
0 missing

107 properties

1025009
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
10
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.
10
Number of numeric attributes.
1
Number of nominal attributes.
Minimal mutual information between the nominal attributes and the target attribute.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
7.01
Maximum of means among attributes of the numeric type.
10
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
2.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.66
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.
Maximum mutual information between the nominal attributes and the target attribute.
-0
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
10
The maximum number of distinct values among attributes of the nominal type.
1.12
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-1.21
Third quartile of kurtosis among attributes of the numeric type.
0.43
Average class difference between consecutive instances.
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Maximum skewness among attributes of the numeric type.
0
Percentage of instances belonging to the least frequent class.
90.91
Percentage of numeric attributes.
7
Third quartile of means among attributes of the numeric type.
0.94
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.74
Maximum standard deviation of attributes of the numeric type.
8
Number of instances belonging to the least frequent class.
9.09
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.14
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
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
0.74
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
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1.29
Mean kurtosis among attributes of the numeric type.
0.5
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.36
First quartile of kurtosis among attributes of the numeric type.
3.74
Third quartile of standard deviation of attributes of the numeric type.
0.94
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4.75
Mean of means among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.5
First quartile of means among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.14
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
0.39
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.74
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
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-0
First quartile of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
10
Average number of distinct values among the attributes of the nominal type.
-0
Mean skewness among attributes of the numeric type.
1.12
First quartile of standard deviation of attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.14
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.43
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.74
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
0.51
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.12
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-1.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.42
Entropy of the target attribute values.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
513701
Number of instances belonging to the most frequent class.
-1.36
Minimum kurtosis among attributes of the numeric type.
4.74
Second quartile (Median) of means among attributes of the numeric type.
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
2.5
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-1.21
Maximum kurtosis among attributes of the numeric type.

7 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
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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