OpenML
ELE-2

ELE-2

active ARFF Public Domain (CC0) Visibility: public Uploaded 19-04-2020 by Rafael Gomes Mantovani
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Electrical-Maintenance data set This problem consists of four input variables and the available data set is comprised of a representative number of well distributed examples. In this case, the learning methods are expected to obtain a considerable number of rules. Therefore, this problem involves a larger search space (high complexity). ###Attributes 1. X1 - real [0.5,11.0] 2. X2 - real [0.15,8.55] 3. X3 - real [1.64,142.5] 4. X4 - real [1.0,165.0] 5. Y - real [64.470001,8546.030273]

5 features

Y (target)numeric1011 unique values
0 missing
X1numeric15 unique values
0 missing
X2numeric37 unique values
0 missing
X3numeric72 unique values
0 missing
X4numeric47 unique values
0 missing

19 properties

1056
Number of instances (rows) of the dataset.
5
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.
5
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
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.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
-1937.9
Average class difference between consecutive instances.
0
Percentage of missing values.

9 tasks

0 runs - estimation_procedure: 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
Define a new task