Data
liver-disorders

liver-disorders

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Author: BUPA Medical Research Ltd. Donor: Richard S. Forsyth Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Liver+Disorders) - 5/15/1990 Please cite: BUPA liver disorders The first 5 variables are all blood tests which are thought to be sensitive to liver disorders that might arise from excessive alcohol consumption. Each line in the dataset constitutes the record of a single male individual. Important note: The 7th field (selector) has been widely misinterpreted in the past as a dependent variable representing presence or absence of a liver disorder. This is incorrect [1]. The 7th field was created by BUPA researchers as a train/test selector. It is not suitable as a dependent variable for classification. The dataset does not contain any variable representing presence or absence of a liver disorder. Researchers who wish to use this dataset as a classification benchmark should follow the method used in experiments by the donor (Forsyth & Rada, 1986, Machine learning: applications in expert systems and information retrieval) and others (e.g. Turney, 1995, Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm), who used the 6th field (drinks), after dichotomising, as a dependent variable for classification. Because of widespread misinterpretation in the past, researchers should take care to state their method clearly. Attribute information 1. mcv mean corpuscular volume 2. alkphos alkaline phosphotase 3. sgpt alanine aminotransferase 4. sgot aspartate aminotransferase 5. gammagt gamma-glutamyl transpeptidase 6. drinks number of half-pint equivalents of alcoholic beverages drunk per day 7. selector field created by the BUPA researchers to split the data into train/test sets [1] McDermott & Forsyth 2016, Diagnosing a disorder in a classification benchmark, Pattern Recognition Letters, Volume 73. Note Forsyth is named on the UCI page as the original donor of the dataset.

7 features

drinks (target)numeric16 unique values
0 missing
mcvnumeric26 unique values
0 missing
alkphosnumeric78 unique values
0 missing
sgptnumeric67 unique values
0 missing
sgotnumeric47 unique values
0 missing
gammagtnumeric94 unique values
0 missing
selector (ignore)nominal2 unique values
0 missing

107 properties

345
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
16
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.
6
Number of numeric attributes.
1
Number of nominal attributes.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
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
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1
Average mutual information between the nominal attributes and the target attribute.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
19.35
First quartile of means among attributes of the numeric type.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
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
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
-1
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
-1
First quartile of mutual information between the nominal attributes and the target attribute.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
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.
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.47
First quartile of skewness among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
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
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.69
Mean skewness among attributes of the numeric type.
4.17
First quartile of standard deviation of attributes of the numeric type.
-1
Second quartile (Median) of entropy among attributes.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
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
-1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
31.3
Percentage of instances belonging to the most frequent class.
15.83
Mean standard deviation of attributes of the numeric type.
5.9
Second quartile (Median) of kurtosis among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
Entropy of the target attribute values.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
108
Number of instances belonging to the most frequent class.
-1
Minimal entropy among attributes.
34.34
Second quartile (Median) of means among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
-1
Maximum entropy among attributes.
0.75
Minimum kurtosis among attributes of the numeric type.
-1
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
13.81
Maximum kurtosis among attributes of the numeric type.
3.46
Minimum of means among attributes of the numeric type.
1.92
Second quartile (Median) of skewness among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
90.16
Maximum of means among attributes of the numeric type.
-1
Minimal mutual information between the nominal attributes and the target attribute.
14.21
Second quartile (Median) of standard deviation of attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
-1
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
14.29
Percentage of binary attributes.
-1
Third quartile of entropy among attributes.
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
-1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.39
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
11.31
Third quartile of kurtosis among attributes of the numeric type.
1.06
Average class difference between consecutive instances.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.06
Maximum skewness among attributes of the numeric type.
3.34
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
74.94
Third quartile of means among attributes of the numeric type.
-1
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
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
39.25
Maximum standard deviation of attributes of the numeric type.
0.29
Percentage of instances belonging to the least frequent class.
85.71
Percentage of numeric attributes.
-1
Third quartile of mutual information between the nominal attributes and the target attribute.
-1
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
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-1
Average entropy of the attributes.
1
Number of instances belonging to the least frequent class.
14.29
Percentage of nominal attributes.
2.92
Third quartile of skewness among attributes of the numeric type.
-1
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
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6.57
Mean kurtosis among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1
First quartile of entropy among attributes.
24.45
Third quartile of standard deviation of attributes of the numeric type.
-1
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
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
42.8
Mean of means among attributes of the numeric type.
-1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.13
First quartile of kurtosis among attributes of the numeric type.

11 tasks

8 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: selector
1 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: selector
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: selector
1 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: selector
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: selector
38 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: drinks
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: selector
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: selector
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: selector
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