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haberman

haberman

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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
Show all 4 features

107 properties

NumberOfInstances
306
Number of instances (rows) of the dataset.
NumberOfFeatures
4
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
2
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
AutoCorrelation
0.79
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
0.83
Entropy of the target attribute values.
DecisionStumpAUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
23.21
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
73.53
Percentage of instances belonging to the most frequent class.
MajorityClassSize
225
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
3.53
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
11.73
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
52.46
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.04
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
12
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
2.98
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
10.8
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
3.53
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
5.57
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
28.24
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.04
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
97.17
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
7
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.57
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
9
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
3.53
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.59
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
4.03
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.04
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.15
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
7.19
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
26.47
Percentage of instances belonging to the least frequent class.
MinorityClassSize
81
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
25
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
50
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
50
Percentage of nominal attributes.
Quartile1AttributeEntropy
3.53
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.59
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
4.03
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.04
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.15
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
7.19
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
3.53
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
5.57
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
28.24
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.04
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.57
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
9
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
3.53
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
11.73
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
52.46
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
2.98
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
10.8
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
7.07
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

20 tasks

Supervised Classification on haberman
1379 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Survival_status
Supervised Classification on haberman
973 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Survival_status
Supervised Classification on haberman
359 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Survival_status
Supervised Classification on haberman
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Survival_status
Supervised Classification on haberman
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Survival_status
Supervised Classification on haberman
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Survival_status
Learning Curve on haberman
210 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Survival_status
Learning Curve on haberman
84 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Survival_status
Supervised Data Stream Classification on haberman
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Survival_status
Clustering on haberman
0 runs
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
Clustering on haberman
0 runs - estimation_procedure: 50 times Clustering
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