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monks-problems-2

monks-problems-2

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  • artificial Machine Learning mythbusting_1 OpenML100 Statistics study_1 study_123 study_135 study_14 study_144 study_15 study_20 study_34 study_41 study_50 study_52 study_7 uci study_293 study_379
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Author: Sebastian Thrun (Carnegie Mellon University) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/MONK's+Problems) - October 1992 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) The Monk's Problems: Problem 2 Once upon a time, in July 1991, the monks of Corsendonk Priory were faced with a school held in their priory, namely the 2nd European Summer School on Machine Learning. After listening more than one week to a wide variety of learning algorithms, they felt rather confused: Which algorithm would be optimal? And which one to avoid? As a consequence of this dilemma, they created a simple task on which all learning algorithms ought to be compared: the three MONK's problems. The target concept associated with the 2nd Monk's problem is the binary outcome of the logical formula: MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1} In this dataset, the original train and test sets were merged to allow other sampling procedures. However, the original train-test splits can be found as one of the OpenML tasks. ### Attribute information: * attr1: 1, 2, 3 * attr2: 1, 2, 3 * attr3: 1, 2 * attr4: 1, 2, 3 * attr5: 1, 2, 3, 4 * attr6: 1, 2 ### Relevant papers 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. Technical Report CS-CMU-91-197, Carnegie Mellon University, Dec. 1991.

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.
REPTreeDepth3AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
2
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3ErrRate
0.38
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
RandomTreeDepth1AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.01
Number of attributes divided by the number of instances.
MaxMutualInformation
0.01
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
42.86
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
255.35
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
4
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.69
Third quartile of entropy among attributes.
RandomTreeDepth1Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.44
Average class difference between consecutive instances.
RandomTreeDepth2AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
34.28
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
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
RandomTreeDepth2ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
1.46
Average entropy of the attributes.
MinorityClassSize
206
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile3MutualInformation
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
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
RandomTreeDepth2Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
1
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
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
RandomTreeDepth3AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
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
RandomTreeDepth3ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
0
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
-0.08
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
RandomTreeDepth3Kappa
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
400.68
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
3
Number of binary attributes.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.38
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
StdvNominalAttDistinctValues
0.76
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
2.71
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
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
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
kNN1NAUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2ErrRate
0.38
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
kNN1NErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
65.72
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.58
Second quartile (Median) of entropy among attributes.
REPTreeDepth2Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
0.93
Entropy of the target attribute values.
kNN1NKappa
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
395
Number of instances belonging to the most frequent class.
MinAttributeEntropy
1
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Show all 107 properties

36 tasks

Supervised Classification on monks-problems-2
261942 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on monks-problems-2
129575 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on monks-problems-2
352 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on monks-problems-2
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on monks-problems-2
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Learning Curve on monks-problems-2
253 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on monks-problems-2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on monks-problems-2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on monks-problems-2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on monks-problems-2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on monks-problems-2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on monks-problems-2
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on monks-problems-2
0 runs - estimation_procedure: 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 - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Data Stream Classification on monks-problems-2
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - target_feature: class
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Clustering on monks-problems-2
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on monks-problems-2
1308 runs - target_feature: class
Subgroup Discovery on monks-problems-2
1307 runs - target_feature: class
Subgroup Discovery on monks-problems-2
0 runs - target_feature: class
Subgroup Discovery on monks-problems-2
0 runs - target_feature: class
Subgroup Discovery on monks-problems-2
0 runs - target_feature: class
Subgroup Discovery on monks-problems-2
0 runs - target_feature: class
Subgroup Discovery on monks-problems-2
0 runs - target_feature: class
Subgroup Discovery on monks-problems-2
0 runs - target_feature: class
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