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monks-problems-3

monks-problems-3

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Author: Sebastian Thrun Source: [original](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - Please cite: The Monk's Problems: Problem 3 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. Sources: (a) Donor: Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA E-mail: thrun@cs.cmu.edu (b) Date: October 1992 Relevant Information: The MONK's problem were the basis of a first international comparison of learning algorithms. The result of this comparison is summarized in "The MONK's Problems - A Performance Comparison of Different Learning algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S. Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec. 1991. One significant characteristic of this comparison is that it was performed by a collection of researchers, each of whom was an advocate of the technique they tested (often they were the creators of the various methods). In this sense, the results are less biased than in comparisons performed by a single person advocating a specific learning method, and more accurately reflect the generalization behavior of the learning techniques as applied by knowledgeable users. There are three MONK's problems. The domains for all MONK's problems are the same (described below). One of the MONK's problems has noise added. For each problem, the domain has been partitioned into a train and test set. Attribute information: 1. class: 0, 1 2. a1: 1, 2, 3 3. a2: 1, 2, 3 4. a3: 1, 2 5. a4: 1, 2, 3 6. a5: 1, 2, 3, 4 7. a6: 1, 2 Target Concepts associated to the MONK's problem: MONK-1: (a1 = a2) or (a5 = 1) MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1} MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3) (5% class noise added to the training set)

7 features

class (target)nominal2 unique values
0 missing
attr1nominal3 unique values
0 missing
attr2nominal3 unique values
0 missing
attr3nominal2 unique values
0 missing
attr4nominal3 unique values
0 missing
attr5nominal4 unique values
0 missing
attr6nominal2 unique values
0 missing

107 properties

554
Number of instances (rows) of the dataset.
7
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.
0
Number of numeric attributes.
7
Number of nominal attributes.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.95
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
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
12.78
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
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
0.76
Standard deviation of the number of distinct values among attributes of the nominal type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.71
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
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
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
51.99
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
1.58
Second quartile (Median) of entropy among attributes.
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
288
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.22
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0.95
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.
0.32
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
42.86
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.69
Third quartile of entropy among attributes.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
9.44
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.77
Average class difference between consecutive instances.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of means among attributes of the numeric type.
0.98
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
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
48.01
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
0.31
Third quartile of mutual information between the nominal attributes and the target attribute.
0.03
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.46
Average entropy of the attributes.
266
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
Third quartile of skewness among attributes of the numeric type.
0.95
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
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
First quartile of entropy among attributes.
Third quartile of standard deviation of attributes of the numeric type.
0.98
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
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.11
Average mutual information between the nominal attributes and the target attribute.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.

13 tasks

8 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: attr5
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: attr3
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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