Data
balance-scale

balance-scale

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title: Balance Scale Weight & Distance Database 2. Source Information: (a) Source: Generated to model psychological experiments reported by Siegler, R. S. (1976). Three Aspects of Cognitive Development. Cognitive Psychology, 8, 481-520. (b) Donor: Tim Hume (hume@ics.uci.edu) (c) Date: 22 April 1994 3. Past Usage: (possibly different formats of this data) - Publications 1. Klahr, D., & Siegler, R.S. (1978). The Representation of Children's Knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), Advances in Child Development and Behavior, pp. 61-116. New York: Academic Press 2. Langley,P. (1987). A General Theory of Discrimination Learning. In D. Klahr, P. Langley, & R. Neches (Eds.), Production System Models of Learning and Development, pp. 99-161. Cambridge, MA: MIT Press 3. Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press 4. McClelland, J.L. (1988). Parallel Distibuted Processing: Implications for Cognition and Development. Technical Report AIP-47, Department of Psychology, Carnegie-Mellon University 5. Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling Cognitive Development on Balance Scale Phenomena. Machine Learning, Vol. 16, pp. 59-88. 4. Relevant Information: 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. 5. Number of Instances: 625 (49 balanced, 288 left, 288 right) 6. Number of Attributes: 4 (numeric) + class name = 5 7. Attribute Information: 1. Class Name: 3 (L, B, R) 2. Left-Weight: 5 (1, 2, 3, 4, 5) 3. Left-Distance: 5 (1, 2, 3, 4, 5) 4. Right-Weight: 5 (1, 2, 3, 4, 5) 5. Right-Distance: 5 (1, 2, 3, 4, 5) 8. Missing Attribute Values: none 9. Class Distribution: 1. 46.08 percent are L 2. 07.84 percent are B 3. 46.08 percent are R

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

107 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.
0.7
Average class difference between consecutive instances.
0.84
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
0.2
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
0.64
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
0.84
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
0.2
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
0.64
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
0.84
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
0.2
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
0.64
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
1.32
Entropy of the target attribute values.
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.01
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
46.08
Percentage of instances belonging to the most frequent class.
288
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
-1.3
Maximum kurtosis among attributes of the numeric type.
3
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
0
Maximum skewness among attributes of the numeric type.
1.42
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
-1.3
Mean kurtosis among attributes of the numeric type.
3
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3
Average number of distinct values among the attributes of the nominal type.
0
Mean skewness among attributes of the numeric type.
1.42
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.3
Minimum kurtosis among attributes of the numeric type.
3
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Minimum skewness among attributes of the numeric type.
1.42
Minimum standard deviation of attributes of the numeric type.
7.84
Percentage of instances belonging to the least frequent class.
49
Number of instances belonging to the least frequent class.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
80
Percentage of numeric attributes.
20
Percentage of nominal attributes.
First quartile of entropy among attributes.
-1.3
First quartile of kurtosis among attributes of the numeric type.
3
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
0
First quartile of skewness among attributes of the numeric type.
1.42
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
-1.3
Second quartile (Median) of kurtosis among attributes of the numeric type.
3
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Second quartile (Median) of skewness among attributes of the numeric type.
1.42
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
-1.3
Third quartile of kurtosis among attributes of the numeric type.
3
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0
Third quartile of skewness among attributes of the numeric type.
1.42
Third quartile of standard deviation of attributes of the numeric type.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.16
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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