Data
haberman

haberman

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
1 likes downloaded by 18 people , 19 total downloads 0 issues 0 downvotes
  • mythbusting_1 study_1 study_123 study_127 study_15 study_20 study_29 study_30 study_41 study_50 study_52 study_7 study_88 uci
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Please cite: 1. Title: Haberman's Survival Data 2. Sources: (a) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) (b) Date: March 4, 1999 3. Past Usage: 1. Haberman, S. J. (1976). Generalized Residuals for Log-Linear Models, Proceedings of the 9th International Biometrics Conference, Boston, pp. 104-122. 2. Landwehr, J. M., Pregibon, D., and Shoemaker, A. C. (1984), Graphical Models for Assessing Logistic Regression Models (with discussion), Journal of the American Statistical Association 79: 61-83. 3. Lo, W.-D. (1993). Logistic Regression Trees, PhD thesis, Department of Statistics, University of Wisconsin, Madison, WI. 4. Relevant Information: The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer. 5. Number of Instances: 306 6. Number of Attributes: 4 (including the class attribute) 7. Attribute Information: 1. Age of patient at time of operation (numerical) 2. Patient's year of operation (year - 1900, numerical) 3. Number of positive axillary nodes detected (numerical) 4. Survival status (class attribute) 1 = the patient survived 5 years or longer 2 = the patient died within 5 year 8. Missing Attribute Values: None Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

4 features

Survival_status (target)nominal2 unique values
0 missing
Age_of_patient_at_time_of_operationnumeric49 unique values
0 missing
Patients_year_of_operationnominal12 unique values
0 missing
Number_of_positive_axillary_nodes_detectednumeric31 unique values
0 missing

107 properties

306
Number of instances (rows) of the dataset.
4
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.
2
Number of numeric attributes.
2
Number of nominal attributes.
97.17
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.
0.04
First quartile of mutual information between the nominal attributes and the target attribute.
0.28
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
7
Average number of distinct values among the attributes of the nominal type.
0.15
First quartile of skewness among attributes of the numeric type.
0.02
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
7.07
Standard deviation of the number of distinct values among attributes of the nominal type.
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.57
Mean skewness among attributes of the numeric type.
7.19
First quartile of standard deviation of attributes of the numeric type.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.26
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
9
Mean standard deviation of attributes of the numeric type.
3.53
Second quartile (Median) of entropy among attributes.
0.28
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
0.34
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
73.53
Percentage of instances belonging to the most frequent class.
3.53
Minimal entropy among attributes.
5.57
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.83
Entropy of the target attribute values.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
225
Number of instances belonging to the most frequent class.
-0.59
Minimum kurtosis among attributes of the numeric type.
28.24
Second quartile (Median) of means among attributes of the numeric type.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.53
Maximum entropy among attributes.
4.03
Minimum of means among attributes of the numeric type.
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.26
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
11.73
Maximum kurtosis among attributes of the numeric type.
0.04
Minimal mutual information between the nominal attributes and the target attribute.
1.57
Second quartile (Median) of skewness among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
52.46
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
25
Percentage of binary attributes.
9
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.53
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.
0.04
Maximum mutual information between the nominal attributes and the target attribute.
0.15
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
3.53
Third quartile of entropy among attributes.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
23.21
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
12
The maximum number of distinct values among attributes of the nominal type.
7.19
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
11.73
Third quartile of kurtosis among attributes of the numeric type.
0.79
Average class difference between consecutive instances.
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.98
Maximum skewness among attributes of the numeric type.
26.47
Percentage of instances belonging to the least frequent class.
50
Percentage of numeric attributes.
52.46
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
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.8
Maximum standard deviation of attributes of the numeric type.
81
Number of instances belonging to the least frequent class.
50
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
0.26
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.53
Average entropy of the attributes.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.53
First quartile of entropy among attributes.
2.98
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.57
Mean kurtosis among attributes of the numeric type.
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.59
First quartile of kurtosis among attributes of the numeric type.
10.8
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
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
28.24
Mean of means among attributes of the numeric type.
0.04
Average mutual information between the nominal attributes and the target attribute.
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.03
First quartile of means among attributes of the numeric type.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.26
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

11 tasks

1377 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Survival_status
973 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Survival_status
359 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Survival_status
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Survival_status
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Survival_status
210 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Survival_status
84 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Survival_status
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Survival_status
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task