Data
balance-scale

balance-scale

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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
Show all 5 features

107 properties

NumberOfInstances
625
Number of instances (rows) of the dataset.
NumberOfFeatures
5
Number of attributes (columns) of the dataset.
NumberOfClasses
3
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
4
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0
Second quartile (Median) of skewness among attributes of the numeric type.
REPTreeDepth3Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
3
Maximum of means among attributes of the numeric type.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
1.42
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1AUC
0.83
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
Maximum mutual information between the nominal attributes and the target attribute.
MinSkewnessOfNumericAtts
0
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
RandomTreeDepth1ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1.42
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
-1.3
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.7
Average class difference between consecutive instances.
RandomTreeDepth1Kappa
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
1.42
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
7.84
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
80
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
3
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
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
RandomTreeDepth2AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
49
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
20
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
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
RandomTreeDepth2ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanKurtosisOfNumericAtts
-1.3
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
0
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
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
RandomTreeDepth2Kappa
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
3
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
-1.3
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1.42
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
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
RandomTreeDepth3AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
3
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
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
RandomTreeDepth3ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
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
RandomTreeDepth3Kappa
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
3
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
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
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
0
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.42
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NErrRate
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
kNN1NAUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanStdDevOfNumericAtts
1.42
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
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
kNN1NErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
46.08
Percentage of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.3
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
1.32
Entropy of the target attribute values.
kNN1NKappa
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
288
Number of instances belonging to the most frequent class.
MinKurtosisOfNumericAtts
-1.3
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
3
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
Maximum entropy among attributes.
MinMeansOfNumericAtts
3
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
-1.3
Maximum kurtosis among attributes of the numeric type.
Show all 107 properties

47 tasks

Supervised Classification on balance-scale
24833 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on balance-scale
303 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on balance-scale
302 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on balance-scale
185 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on balance-scale
43 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on balance-scale
32 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on balance-scale
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on balance-scale
0 runs - estimation_procedure: 100 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on balance-scale
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on balance-scale
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on balance-scale
306 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on balance-scale
179 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: pattern_team_auroc10 - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: pattern_team_auroc10 - target_feature: class
Learning Curve on balance-scale
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on balance-scale
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - target_feature: class
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Clustering on balance-scale
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on balance-scale
1306 runs - target_feature: class
Subgroup Discovery on balance-scale
1302 runs - target_feature: class
Subgroup Discovery on balance-scale
1300 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
Subgroup Discovery on balance-scale
0 runs - target_feature: class
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