Data
balance-scale

balance-scale

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • artificial OpenML-CC18 OpenML100 shark-tutorial-demo-tag study_1 study_123 study_135 study_14 study_34 study_37 study_41 study_50 study_52 study_7 study_70 study_98 study_99 uci
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Author: Siegler, R. S. (donated by Tim Hume) Source: [UCI](http://archive.ics.uci.edu/ml/datasets/balance+scale) - 1994 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Balance Scale Weight & Distance Database This data set was generated to model psychological experimental results. Each example is classified as having the balance scale tip to the right, tip to the left, or be balanced. The attributes are the left weight, the left distance, the right weight, and the right distance. The correct way to find the class is the greater of (left-distance * left-weight) and (right-distance * right-weight). If they are equal, it is balanced. ### Attribute description The attributes are the left weight, the left distance, the right weight, and the right distance. ### Relevant papers Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling Cognitive Development on Balance Scale Phenomena. Machine Learning, Vol. 16, pp. 59-88.

5 features

class (target)nominal3 unique values
0 missing
left-weightnumeric5 unique values
0 missing
left-distancenumeric5 unique values
0 missing
right-weightnumeric5 unique values
0 missing
right-distancenumeric5 unique values
0 missing

19 properties

625
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.
7.84
Percentage of instances belonging to the least frequent class.
49
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.7
Average class difference between consecutive instances.
0
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
80
Percentage of numeric attributes.
46.08
Percentage of instances belonging to the most frequent class.
20
Percentage of nominal attributes.
288
Number of instances belonging to the most frequent class.

29 tasks

10979 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
304 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
302 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
186 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
48 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
305 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
179 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - 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
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1306 runs - target_feature: class
1302 runs - target_feature: class
1300 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
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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