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ada_prior

ada_prior

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch) Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php Modified by TunedIT (converted to ARFF format) ADA is the marketing database The task of ADA is to discover high revenue people from census data. This is a two-class classification problem. The raw data from the census bureau is known as the Adult database in the UCI machine-learning repository. The 14 original attributes (features) include age, workclass, education, education, marital status, occupation, native country, etc. It contains continuous, binary and categorical features. This dataset is from "prior knowledge track", i.e. has access to the original features and their identity. Number of examples: Pos_ex Neg_ex Tot_ex Train 1029 3118 4147 Valid 103 312 415 This dataset contains samples from both training and validation datasets. ### Attribute information 1. age Instance’s age (numeric) 2. workclass Instance’s work class (nominal) 3. fnlwgt Instance’s sampling weight (numeric) 4. education Instance’s education level (nominal) 5. educationNum Instance’s education level (numeric version) 6. maritalStatus Instance’s marital status (nominal) 7. occupation Instance’s occupation (nominal) 8. relationship Instance’s type of relationship (nominal) 9. race Instance’s race (nominal) 10. sex Instance’s sex (nominal) 11. capitalGain Instance’s capital gain (numeric) 12. capitalLoss Instance’s capital loss (numeric) 13. hoursPerWeek Instance’s number of working hours (numeric) 14. nativeCountry Instance’s native country (numeric) 15. label Class attribute (1: the instance earns more than 50K a year; -1 otherwise)

15 features

label (target)nominal2 unique values
0 missing
agenumeric70 unique values
0 missing
workclassnominal7 unique values
0 missing
fnlwgtnumeric4222 unique values
0 missing
educationnominal16 unique values
0 missing
educationNumnumeric16 unique values
0 missing
maritalStatusnominal7 unique values
0 missing
occupationnominal14 unique values
0 missing
relationshipnominal6 unique values
0 missing
racenominal5 unique values
0 missing
sexnominal2 unique values
0 missing
capitalGainnumeric72 unique values
0 missing
capitalLossnumeric57 unique values
0 missing
hoursPerWeeknumeric77 unique values
0 missing
nativeCountrynominal39 unique values
88 missing

107 properties

4562
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
88
Number of missing values in the dataset.
88
Number of instances with at least one value missing.
6
Number of numeric attributes.
9
Number of nominal attributes.
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
191077.23
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
0.99
Second quartile (Median) of skewness among attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.16
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
13.33
Percentage of binary attributes.
218.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
11.32
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
39
The maximum number of distinct values among attributes of the nominal type.
-0.29
Minimum skewness among attributes of the numeric type.
1.93
Percentage of instances having missing values.
2.7
Third quartile of entropy among attributes.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.75
Maximum skewness among attributes of the numeric type.
2.53
Minimum standard deviation of attributes of the numeric type.
0.13
Percentage of missing values.
43.88
Third quartile of kurtosis among attributes of the numeric type.
0.62
Average class difference between consecutive instances.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
108007.33
Maximum standard deviation of attributes of the numeric type.
24.81
Percentage of instances belonging to the least frequent class.
40
Percentage of numeric attributes.
48677.14
Third quartile of means among attributes of the numeric type.
0.85
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.79
Average entropy of the attributes.
1132
Number of instances belonging to the least frequent class.
60
Percentage of nominal attributes.
0.13
Third quartile of mutual information between the nominal attributes and the target attribute.
0.15
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
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
24.64
Mean kurtosis among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.9
First quartile of entropy among attributes.
5.95
Third quartile of skewness among attributes of the numeric type.
0.56
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
32078.85
Mean of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.5
First quartile of kurtosis among attributes of the numeric type.
33323.75
Third quartile of standard deviation of attributes of the numeric type.
0.85
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.07
Average mutual information between the nominal attributes and the target attribute.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
31.47
First quartile of means among attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
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
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
24.06
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.56
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
0.85
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
11.58
Standard deviation of the number of distinct values among attributes of the nominal type.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
10.89
Average number of distinct values among the attributes of the nominal type.
0.07
First quartile of skewness among attributes of the numeric type.
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.83
Mean skewness among attributes of the numeric type.
9.53
First quartile of standard deviation of attributes of the numeric type.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.56
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
0.23
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
75.19
Percentage of instances belonging to the most frequent class.
19481.17
Mean standard deviation of attributes of the numeric type.
1.62
Second quartile (Median) of entropy among attributes.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.81
Entropy of the target attribute values.
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
3430
Number of instances belonging to the most frequent class.
0.82
Minimal entropy among attributes.
3.77
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
3.4
Maximum entropy among attributes.
-0.05
Minimum kurtosis among attributes of the numeric type.
68.34
Second quartile (Median) of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
121.7
Maximum kurtosis among attributes of the numeric type.
10.15
Minimum of means among attributes of the numeric type.
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

16 tasks

520 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: label
189 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: label
0 runs - estimation_procedure: 33% Holdout set - target_feature: label
69 runs - estimation_procedure: 10-fold Learning Curve - target_feature: label
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: label
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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