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hayes-roth

hayes-roth

active ARFF Publicly available Visibility: public Uploaded 26-08-2014 by Joaquin Vanschoren
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Author: Barbara and Frederick Hayes-Roth Source: [original](https://archive.ics.uci.edu/ml/datasets/Hayes-Roth) - Please cite: Hayes-Roth Database This is a merged version of the separate train and test set which are usually distributed. On OpenML this train-test split can be found as one of the possible tasks. Source Information: (a) Creators: Barbara and Frederick Hayes-Roth (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779 (c) Date: March, 1989 Attribute Information: -- 1. name: distinct for each instance and represented numerically -- 2. hobby: nominal values ranging between 1 and 3 -- 3. age: nominal values ranging between 1 and 4 -- 4. educational level: nominal values ranging between 1 and 4 -- 5. marital status: nominal values ranging between 1 and 4 -- 6. class: nominal value between 1 and 3 Detailed description of the experiment: 1. 3 categories (1, 2, and neither -- which I call 3) -- some of the instances could be classified in either class 1 or 2, and they have been evenly distributed between the two classes 2. 5 Attributes -- A. name (a randomly-generated number between 1 and 132) -- B. hobby (a randomly-generated number between 1 and 3) -- C. age (a number between 1 and 4) -- D. education level (a number between 1 and 4) -- E. marital status (a number between 1 and 4) 3. Classification: -- only attributes C-E are diagnostic; values for A and B are ignored -- Class Neither: if a 4 occurs for any attribute C-E -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E 4. Prototypes: -- Class 1: 111 -- Class 2: 222 -- Class Either: 333 -- Class Neither: 444

5 features

class (target)nominal3 unique values
0 missing
hobbynumeric3 unique values
0 missing
agenumeric4 unique values
0 missing
educational_levelnumeric4 unique values
0 missing
marital_statusnumeric4 unique values
0 missing

107 properties

160
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
3
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.
4
Number of numeric attributes.
1
Number of nominal attributes.
1.94
Mean of means among attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.24
First quartile of kurtosis among attributes of the numeric type.
0.94
Third quartile of standard deviation of attributes of the numeric type.
0.92
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.86
First quartile of means among attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.14
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
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.77
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
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
0.42
First quartile of skewness among attributes of the numeric type.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.92
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.57
Mean skewness among attributes of the numeric type.
0.86
First quartile of standard deviation of attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.14
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.91
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.77
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
0.37
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
40.63
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.49
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.52
Entropy of the target attribute values.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
65
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
-1.49
Minimum kurtosis among attributes of the numeric type.
1.98
Second quartile (Median) of means among attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.49
Maximum kurtosis among attributes of the numeric type.
1.83
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.53
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.98
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.65
Second quartile (Median) of skewness among attributes of the numeric type.
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.94
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Number of attributes divided by the number of instances.
3
The maximum number of distinct values among attributes of the nominal type.
0.34
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.65
Maximum skewness among attributes of the numeric type.
0.84
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.49
Third quartile of kurtosis among attributes of the numeric type.
0.41
Average class difference between consecutive instances.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
Maximum standard deviation of attributes of the numeric type.
19.38
Percentage of instances belonging to the least frequent class.
80
Percentage of numeric attributes.
1.98
Third quartile of means among attributes of the numeric type.
0.92
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
31
Number of instances belonging to the least frequent class.
20
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.14
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
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-0.74
Mean kurtosis among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.65
Third quartile of skewness among attributes of the numeric type.
0.77
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
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

16 tasks

349 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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