Data
ilpd

ilpd

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Author: Bendi Venkata Ramana, M. Surendra Prasad Babu, N. B. Venkateswarlu Source: UCI Please cite: Source: 1. Bendi Venkata Ramana, ramana.bendi '@' gmail.com Associate Professor, Department of Information Technology, Aditya Instutute of Technology and Management, Tekkali - 532201, Andhra Pradesh, India. 2. Prof. M. Surendra Prasad Babu drmsprasadbabu '@' yahoo.co.in Deptartment of Computer Science & Systems Engineering, Andhra University College of Engineering, Visakhapatnam-530 003 Andhra Pradesh, India. 3.Prof. N. B. Venkateswarlu venkat_ritch '@' yahoo.com Department of Computer Science and Engineering, Aditya Instutute of Technology and Management, Tekkali - 532201, Andhra Pradesh, India. Data Set Information: This data set contains 416 liver patient records and 167 non liver patient records.The data set was collected from north east of Andhra Pradesh, India. Selector is a class label used to divide into groups(liver patient or not). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age "90". Attribute Information: 1. Age Age of the patient 2. Gender Gender of the patient 3. TB Total Bilirubin 4. DB Direct Bilirubin 5. Alkphos Alkaline Phosphotase 6. Sgpt Alamine Aminotransferase 7. Sgot Aspartate Aminotransferase 8. TP Total Protiens 9. ALB Albumin 10. A/G Ratio Albumin and Globulin Ratio 11. Selector field used to split the data into two sets (labeled by the experts) Relevant Papers: 1. Bendi Venkata Ramana, Prof. M. S. Prasad Babu and Prof. N. B. Venkateswarlu, “A Critical Comparative Study of Liver Patients from USA and INDIA: An Exploratory Analysis”, International Journal of Computer Science Issues, ISSN :1694-0784, May 2012. 2. Bendi Venkata Ramana, Prof. M. S. Prasad Babu and Prof. N. B. Venkateswarlu, “A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis”, International Journal of Database Management Systems (IJDMS), Vol.3, No.2, ISSN : 0975-5705, PP 101-114, May 2011.

11 features

Class (target)nominal2 unique values
0 missing
V1numeric72 unique values
0 missing
V2nominal2 unique values
0 missing
V3numeric113 unique values
0 missing
V4numeric80 unique values
0 missing
V5numeric263 unique values
0 missing
V6numeric152 unique values
0 missing
V7numeric177 unique values
0 missing
V8numeric58 unique values
0 missing
V9numeric40 unique values
0 missing
V10numeric70 unique values
0 missing

107 properties

583
Number of instances (rows) of the dataset.
11
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.
9
Number of numeric attributes.
2
Number of nominal attributes.
0.61
Average class difference between consecutive instances.
0.6
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
0.33
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
0.15
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
0.6
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
0.33
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
0.15
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
0.6
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
0.33
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
0.15
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
0.86
Entropy of the target attribute values.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.29
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.02
Number of attributes divided by the number of instances.
181.13
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
71.36
Percentage of instances belonging to the most frequent class.
416
Number of instances belonging to the most frequent class.
0.8
Maximum entropy among attributes.
150.92
Maximum kurtosis among attributes of the numeric type.
290.58
Maximum of means among attributes of the numeric type.
0
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
10.55
Maximum skewness among attributes of the numeric type.
288.92
Maximum standard deviation of attributes of the numeric type.
0.8
Average entropy of the attributes.
30.04
Mean kurtosis among attributes of the numeric type.
60.14
Mean of means among attributes of the numeric type.
0
Average mutual information between the nominal attributes and the target attribute.
166.89
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Average number of distinct values among the attributes of the nominal type.
3.29
Mean skewness among attributes of the numeric type.
82.43
Mean standard deviation of attributes of the numeric type.
0.8
Minimal entropy among attributes.
-0.56
Minimum kurtosis among attributes of the numeric type.
0.95
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
-0.29
Minimum skewness among attributes of the numeric type.
0.32
Minimum standard deviation of attributes of the numeric type.
28.64
Percentage of instances belonging to the least frequent class.
167
Number of instances belonging to the least frequent class.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.44
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2
Number of binary attributes.
18.18
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
81.82
Percentage of numeric attributes.
18.18
Percentage of nominal attributes.
0.8
First quartile of entropy among attributes.
-0.08
First quartile of kurtosis among attributes of the numeric type.
2.31
First quartile of means among attributes of the numeric type.
0
First quartile of mutual information between the nominal attributes and the target attribute.
-0.04
First quartile of skewness among attributes of the numeric type.
0.94
First quartile of standard deviation of attributes of the numeric type.
0.8
Second quartile (Median) of entropy among attributes.
11.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
6.48
Second quartile (Median) of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3.21
Second quartile (Median) of skewness among attributes of the numeric type.
6.21
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.8
Third quartile of entropy among attributes.
43.87
Third quartile of kurtosis among attributes of the numeric type.
95.31
Third quartile of means among attributes of the numeric type.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
5.73
Third quartile of skewness among attributes of the numeric type.
212.78
Third quartile of standard deviation of attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.37
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
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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