Data
pbcseq

pbcseq

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized binarized_regression_problem mythbusting_1 study_1 study_144 study_15 study_20 study_41
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

19 features

binaryClass (target)nominal2 unique values
0 missing
case_numbernumeric312 unique values
0 missing
number_of_daysnumeric305 unique values
0 missing
statusnumeric3 unique values
0 missing
drugnominal2 unique values
0 missing
agenumeric308 unique values
0 missing
sexnominal2 unique values
0 missing
daynominal1024 unique values
0 missing
presence_of_asictesnominal2 unique values
60 missing
presence_of_hepatomegalynominal2 unique values
61 missing
presence_of_spidersnominal2 unique values
58 missing
presence_of_edemanumeric3 unique values
0 missing
serum_bilirubinnumeric193 unique values
0 missing
serum_cholesterolnumeric375 unique values
821 missing
albuminnumeric254 unique values
0 missing
alkaline_phosphatasenumeric1263 unique values
60 missing
SGOTnumeric418 unique values
0 missing
plateletsnumeric414 unique values
73 missing
prothrombin_timenumeric78 unique values
0 missing

107 properties

1945
Number of instances (rows) of the dataset.
19
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
1133
Number of missing values in the dataset.
832
Number of instances with at least one value missing.
12
Number of numeric attributes.
7
Number of nominal attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
973
Number of instances belonging to the most frequent class.
0.43
Minimal entropy among attributes.
4.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
8.75
Maximum entropy among attributes.
-1.78
Minimum kurtosis among attributes of the numeric type.
129.03
Second quartile (Median) of means among attributes of the numeric type.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.36
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
77.02
Maximum kurtosis among attributes of the numeric type.
0.18
Minimum of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
17992.08
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1.2
Second quartile (Median) of skewness among attributes of the numeric type.
0.64
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.
0.54
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
31.58
Percentage of binary attributes.
82
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
9.17
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1024
The maximum number of distinct values among attributes of the nominal type.
-0.09
Minimum skewness among attributes of the numeric type.
42.78
Percentage of instances having missing values.
2.94
Third quartile of entropy among attributes.
0.85
Average class difference between consecutive instances.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.24
Maximum skewness among attributes of the numeric type.
0.32
Minimum standard deviation of attributes of the numeric type.
3.07
Percentage of missing values.
25.06
Third quartile of kurtosis among attributes of the numeric type.
0.7
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3675.03
Maximum standard deviation of attributes of the numeric type.
49.97
Percentage of instances belonging to the least frequent class.
63.16
Percentage of numeric attributes.
1116.55
Third quartile of means among attributes of the numeric type.
0.18
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
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
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.1
Average entropy of the attributes.
972
Number of instances belonging to the least frequent class.
36.84
Percentage of nominal attributes.
4.03
Third quartile of skewness among attributes of the numeric type.
0.33
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
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
15.3
Mean kurtosis among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.51
First quartile of entropy among attributes.
938.4
Third quartile of standard deviation of attributes of the numeric type.
0.7
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1928.84
Mean of means among attributes of the numeric type.
0.32
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.7
First quartile of kurtosis among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.11
Average mutual information between the nominal attributes and the target attribute.
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.46
First quartile of means among attributes of the numeric type.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
18.23
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
6
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.7
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
386.28
Standard deviation of the number of distinct values among attributes of the nominal type.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
148
Average number of distinct values among the attributes of the nominal type.
0.2
First quartile of skewness among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.02
Mean skewness among attributes of the numeric type.
1.08
First quartile of standard deviation of attributes of the numeric type.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.32
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.03
Percentage of instances belonging to the most frequent class.
548.25
Mean standard deviation of attributes of the numeric type.
0.94
Second quartile (Median) of entropy among attributes.

6 tasks

467 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
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