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monks-problems-2

monks-problems-2

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Author: Sebastian Thrun Source: [original](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992 Please cite: The Monk's Problems: Problem 2 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 8. 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
Show all 7 features

107 properties

NumberOfInstances
601
Number of instances (rows) of the dataset.
NumberOfFeatures
7
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
0
Number of numeric attributes.
NumberOfSymbolicFeatures
7
Number of nominal attributes.
AutoCorrelation
0.44
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
0.34
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
0.34
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
0.34
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
0.93
Entropy of the target attribute values.
DecisionStumpAUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
255.35
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.03
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.03
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
65.72
Percentage of instances belonging to the most frequent class.
MajorityClassSize
395
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
2
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.01
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
4
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.46
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
MeanMutualInformation
0
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
400.68
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.71
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
1
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
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
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
34.28
Percentage of instances belonging to the least frequent class.
MinorityClassSize
206
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
-0.08
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
3
Number of binary attributes.
PercentageOfBinaryFeatures
42.86
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
1
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.58
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.69
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
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
0.38
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
-0.01
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
0.38
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
-0.01
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
0.38
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0.76
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

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