Data
sick

sick

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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30 features

Class (target)nominal2 unique values
0 missing
agenumeric93 unique values
1 missing
sexnominal2 unique values
150 missing
on_thyroxinenominal2 unique values
0 missing
query_on_thyroxinenominal2 unique values
0 missing
on_antithyroid_medicationnominal2 unique values
0 missing
sicknominal2 unique values
0 missing
pregnantnominal2 unique values
0 missing
thyroid_surgerynominal2 unique values
0 missing
I131_treatmentnominal2 unique values
0 missing
query_hypothyroidnominal2 unique values
0 missing
query_hyperthyroidnominal2 unique values
0 missing
lithiumnominal2 unique values
0 missing
goitrenominal2 unique values
0 missing
tumornominal2 unique values
0 missing
hypopituitarynominal2 unique values
0 missing
psychnominal2 unique values
0 missing
TSH_measurednominal2 unique values
0 missing
TSHnumeric287 unique values
369 missing
T3_measurednominal2 unique values
0 missing
T3numeric69 unique values
769 missing
TT4_measurednominal2 unique values
0 missing
TT4numeric241 unique values
231 missing
T4U_measurednominal2 unique values
0 missing
T4Unumeric146 unique values
387 missing
FTI_measurednominal2 unique values
0 missing
FTInumeric234 unique values
385 missing
TBG_measurednominal1 unique values
0 missing
TBGnumeric0 unique values
3772 missing
referral_sourcenominal5 unique values
0 missing

107 properties

3772
Number of instances (rows) of the dataset.
30
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
6064
Number of missing values in the dataset.
3772
Number of instances with at least one value missing.
7
Number of numeric attributes.
23
Number of nominal attributes.
100
Percentage of instances having missing values.
0.48
Third quartile of entropy among attributes.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
64.93
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
5
The maximum number of distinct values among attributes of the nominal type.
1.23
Minimum skewness among attributes of the numeric type.
5.36
Percentage of missing values.
90.94
Third quartile of kurtosis among attributes of the numeric type.
0.89
Average class difference between consecutive instances.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
13.88
Maximum skewness among attributes of the numeric type.
0.2
Minimum standard deviation of attributes of the numeric type.
23.33
Percentage of numeric attributes.
108.86
Third quartile of means among attributes of the numeric type.
0.93
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
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
35.6
Maximum standard deviation of attributes of the numeric type.
6.12
Percentage of instances belonging to the least frequent class.
76.67
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
0.03
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.34
Average entropy of the attributes.
231
Number of instances belonging to the least frequent class.
0.1
First quartile of entropy among attributes.
4.94
Third quartile of skewness among attributes of the numeric type.
0.78
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
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
51.41
Mean kurtosis among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
5.98
First quartile of kurtosis among attributes of the numeric type.
33.72
Third quartile of standard deviation of attributes of the numeric type.
0.93
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
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
46.44
Mean of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.76
First quartile of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Average mutual information between the nominal attributes and the target attribute.
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.78
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
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
64.95
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
21
Number of binary attributes.
1.26
First quartile of skewness among attributes of the numeric type.
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.93
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
0.67
Standard deviation of the number of distinct values among attributes of the nominal type.
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.09
Average number of distinct values among the attributes of the nominal type.
0.67
First quartile of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
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
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.57
Mean skewness among attributes of the numeric type.
0.26
Second quartile (Median) of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.78
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
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
93.88
Percentage of instances belonging to the most frequent class.
19.05
Mean standard deviation of attributes of the numeric type.
8.87
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
Entropy of the target attribute values.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
3541
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
28.41
Second quartile (Median) of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.52
Maximum entropy among attributes.
4.07
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.03
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
238.18
Maximum kurtosis among attributes of the numeric type.
0.99
Minimum of means among attributes of the numeric type.
1.54
Second quartile (Median) of skewness among attributes of the numeric type.
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
110.47
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
70
Percentage of binary attributes.
22.3
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.79
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.
0.06
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.

11 tasks

0 runs - estimation_procedure: Test on Training Data - 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: 20% Holdout (Ordered) - 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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