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mv

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  • OpenML-Reg19 study_130 synthetic
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Author: Luis Torgo Source: [original](http://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html) - Please cite: This is an artificial data set with dependencies between the attribute values. The cases are generated using the following method: X1 : uniformly distributed over [-5,5] X2 : uniformly distributed over [-15,-10] X3 : IF (X1 > 0) THEN X3 = green ELSE X3 = red with probability 0.4 and X4=brown with prob. 0.6 X4 : IF (X3=green) THEN X4=X1+2X2 ELSE X4=X1/2 with prob. 0.3, and X4=X2/2 with prob. 0.7 X5 : uniformly distributed over [-1,1] X6 : X6=X4*[epsilon], where [epsilon] is uniformly distribute over [0,5] X7 : X7=yes with prob. 0.3 and X7=no with prob. 0.7 X8 : IF (X5 < 0.5) THEN X8 = normal ELSE X8 = large X9 : uniformly distributed over [100,500] X10 : uniformly distributed integer over the interval [1000,1200] Obtain the value of the target variable Y using the rules: IF (X2 > 2 ) THEN Y = 35 - 0.5 X4 ELSE IF (-2 <= X4 <= 2) THEN Y = 10 - 2 X1 ELSE IF (X7 = yes) THEN Y = 3 -X1/X4 ELSE IF (X8 = normal) THEN Y = X6 + X1 ELSE Y = X1/2 Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html

11 features

y (target)numeric39029 unique values
0 missing
x1numeric40105 unique values
0 missing
x2numeric27796 unique values
0 missing
x3nominal3 unique values
0 missing
x4numeric39011 unique values
0 missing
x5numeric40418 unique values
0 missing
x6numeric39833 unique values
0 missing
x7nominal2 unique values
0 missing
x8nominal2 unique values
0 missing
x9numeric38738 unique values
0 missing
x10numeric201 unique values
0 missing

107 properties

40768
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
0
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.
8
Number of numeric attributes.
3
Number of nominal attributes.
Minimal mutual information between the nominal attributes and the target attribute.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1100.27
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
18.18
Percentage of binary attributes.
6.72
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.83
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3
The maximum number of distinct values among attributes of the nominal type.
0.58
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.68
Third quartile of kurtosis among attributes of the numeric type.
-10.42
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.97
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
72.73
Percentage of numeric attributes.
224.42
Third quartile of means among attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
115.27
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
27.27
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
-1
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
170.34
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.2
First quartile of kurtosis among attributes of the numeric type.
46.24
Third quartile of standard deviation of attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-10.49
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.33
Average number of distinct values among the attributes of the nominal type.
-0.24
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.58
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.02
Mean skewness among attributes of the numeric type.
1.81
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
25.31
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-1.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
-1.21
Minimum kurtosis among attributes of the numeric type.
-2.2
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-12.5
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.47
Maximum kurtosis among attributes of the numeric type.

5 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: y
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: y
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task