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monks-problems-1

monks-problems-1

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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 1 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 1st Monk's problem is the binary outcome of the logical formula: MONK-1: (a1 == a2) or (a5 == 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

62 properties

556
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.
100
Percentage of nominal attributes.
0.08
Third quartile of mutual information between the nominal attributes and the target attribute.
4
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
1
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
50
Percentage of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.76
Standard deviation of the number of distinct values among attributes of the nominal type.
1.46
Average entropy of the attributes.
278
Number of instances belonging to the least frequent class.
0
First quartile of mutual information between the nominal attributes and the target attribute.
Mean kurtosis among attributes of the numeric type.
3
Number of binary attributes.
First quartile of skewness among attributes of the numeric type.
Mean of means among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.82
Average class difference between consecutive instances.
0.05
Average mutual information between the nominal attributes and the target attribute.
1.58
Second quartile (Median) of entropy among attributes.
1
Entropy of the target attribute values.
27.14
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
2.71
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of means among attributes of the numeric type.
19.29
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
50
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of skewness among attributes of the numeric type.
278
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
2
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
42.86
Percentage of binary attributes.
1.69
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.31
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.

27 tasks

239377 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
116166 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
366 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
207 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class
215 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 - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
1304 runs - target_feature: class
1303 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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