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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

19 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.
18.18
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
-10.42
Average class difference between consecutive instances.
72.73
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
Percentage of instances belonging to the most frequent class.
27.27
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

14 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: 33% Holdout set - target_feature: y
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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