Data
aids

aids

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Author: Jeffrey S. Simonoff Source: [original](http://www.stern.nyu.edu/~jsimonof/AnalCatData) - Please cite: Jeffrey S. Simonoff. Analyzing Categorical Data, Springer-Verlag, New York, 2003 Data originating from the book "Analyzing Categorical Data" by Jeffrey S. Simonoff.

5 features

Sex (target)nominal2 unique values
0 missing
Agenominal5 unique values
0 missing
Racenominal5 unique values
0 missing
AIDSnumeric50 unique values
0 missing
Totalnumeric50 unique values
0 missing

19 properties

50
Number of instances (rows) of the dataset.
5
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.
3
Number of nominal attributes.
20
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.98
Average class difference between consecutive instances.
0
Percentage of missing values.
0.1
Number of attributes divided by the number of instances.
40
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
60
Percentage of nominal attributes.
25
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the least frequent class.
25
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

15 tasks

498 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Sex
369 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Sex
219 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Sex
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Sex
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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