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analcatdata_homerun

analcatdata_homerun

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: analcatdata A collection of data sets used in the book "Analyzing Categorical Data," by Jeffrey S. Simonoff, Springer-Verlag, New York, 2003. The submission consists of a zip file containing two versions of each of 84 data sets, plus this README file. Each data set is given in comma-delimited ASCII (.csv) form, and Microsoft Excel (.xls) form. NOTICE: These data sets may be used freely for scientific, educational and/or noncommercial purposes, provided suitable acknowledgment is given (by citing the above-named reference). Further details concerning the book, including information on statistical software (including sample S-PLUS/R and SAS code), are available at the web site http://www.stern.nyu.edu/~jsimonof/AnalCatData Information about the dataset CLASSTYPE: nominal CLASSINDEX: none specific Note: Quotes, Single-Quotes and Backslashes were removed, Blanks replaced with Underscores

28 features

Bonds.HR (target)nominal4 unique values
1 missing
Game (ignore)numeric163 unique values
0 missing
StL.monthnominal7 unique values
0 missing
StL.datenumeric31 unique values
0 missing
StL.season.daynumeric159 unique values
0 missing
StL.homenominal2 unique values
0 missing
StL.runsnumeric17 unique values
0 missing
StL.opp.runsnumeric15 unique values
0 missing
StL.winnominal3 unique values
0 missing
McGwire.HRnominal4 unique values
0 missing
McGwire.outnominal2 unique values
0 missing
Chi.monthnominal7 unique values
0 missing
Chi.datenumeric31 unique values
0 missing
Chi.season.daynumeric161 unique values
0 missing
Chi.homenominal2 unique values
0 missing
Chi.runsnumeric15 unique values
0 missing
Chi.opp.runsnumeric15 unique values
0 missing
Chi.winnominal2 unique values
0 missing
Sosa.HRnominal4 unique values
0 missing
Sosa.outnominal2 unique values
0 missing
SF.monthnominal7 unique values
1 missing
SF.datenumeric31 unique values
1 missing
SF.season.daynumeric162 unique values
1 missing
SF.homenominal2 unique values
1 missing
SF.runsnumeric13 unique values
1 missing
SF.opp.runsnumeric15 unique values
1 missing
SF.winnominal2 unique values
1 missing
Bonds.outnominal2 unique values
1 missing

107 properties

163
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
5
Number of distinct values of the target attribute (if it is nominal).
9
Number of missing values in the dataset.
1
Number of instances with at least one value missing.
13
Number of numeric attributes.
15
Number of nominal attributes.
32.49
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
8
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.47
Average number of distinct values among the attributes of the nominal type.
-0.04
First quartile of skewness among attributes of the numeric type.
-0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
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
2
Standard deviation of the number of distinct values among attributes of the nominal type.
0.44
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.36
Mean skewness among attributes of the numeric type.
3.15
First quartile of standard deviation of attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.38
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
0.43
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
-0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
16.92
Mean standard deviation of attributes of the numeric type.
1
Second quartile (Median) of entropy among attributes.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
0.57
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
61.96
Percentage of instances belonging to the most frequent class.
0.17
Minimal entropy among attributes.
-0.8
Second quartile (Median) of kurtosis among attributes of the numeric type.
-0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.24
Entropy of the target attribute values.
-0.12
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
101
Number of instances belonging to the most frequent class.
-1.32
Minimum kurtosis among attributes of the numeric type.
10.24
Second quartile (Median) of means among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.71
Maximum entropy among attributes.
4.62
Minimum of means among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.43
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.08
Maximum kurtosis among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
0.31
Second quartile (Median) of skewness among attributes of the numeric type.
-0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
92.83
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
28.57
Percentage of binary attributes.
5.96
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.17
Number of attributes divided by the number of instances.
0.09
Maximum mutual information between the nominal attributes and the target attribute.
-0.05
Minimum skewness among attributes of the numeric type.
0.61
Percentage of instances having missing values.
1.57
Third quartile of entropy among attributes.
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
33.88
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
7
The maximum number of distinct values among attributes of the nominal type.
2.94
Minimum standard deviation of attributes of the numeric type.
0.2
Percentage of missing values.
0.19
Third quartile of kurtosis among attributes of the numeric type.
0.46
Average class difference between consecutive instances.
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.06
Maximum skewness among attributes of the numeric type.
0.61
Percentage of instances belonging to the least frequent class.
46.43
Percentage of numeric attributes.
72.99
Third quartile of means among attributes of the numeric type.
0.5
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.44
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
52.65
Maximum standard deviation of attributes of the numeric type.
1
Number of instances belonging to the least frequent class.
53.57
Percentage of nominal attributes.
0.07
Third quartile of mutual information between the nominal attributes and the target attribute.
0.38
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
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.22
Average entropy of the attributes.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.82
First quartile of entropy among attributes.
0.73
Third quartile of skewness among attributes of the numeric type.
0
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
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.46
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.5
Mean kurtosis among attributes of the numeric type.
0.49
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.21
First quartile of kurtosis among attributes of the numeric type.
41.64
Third quartile of standard deviation of attributes of the numeric type.
0.5
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.44
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
29.45
Mean of means among attributes of the numeric type.
0.04
Average mutual information between the nominal attributes and the target attribute.
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.88
First quartile of means among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.38
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
0.45
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

3 tasks

0 runs - estimation_procedure: 50 times Clustering
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