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fri_c4_500_50

fri_c4_500_50

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: The Friedman datasets are 80 artificially generated datasets originating from: J.H. Friedman (1999). Stochastic Gradient Boosting The dataset names are coded as "fri_colinearintydegree_samplenumber_featurenumber". Friedman is the one of the most used functions for data generation (Friedman, 1999). Friedman functions include both linear and non-linear relations between input and output, and a normalized noise (e) is added to the output. The Friedman function is as follows: y=10*sin(pi*x1*x2)+20*(x3-0.5)^2=10*X4+5*X5+e In the original Friedman function, there are 5 features for input. To measure the effects of non-related features, additional features are added to the datasets. These added features are independent from the output. However, to measure the algorithm's robustness to the colinearity, the datasets are generated with 5 different colinearity degrees. The colinearity degrees is the number of features depending on other features. The generated Friedman dataset's parameters and values are given below: The number of features: 5 10 25 50 100 (only the first 5 features are related to the output. The rest are completely random) The number of samples: 100 250 500 1000 Colinearity degrees: 0 1 2 3 4 For the datasets with colinearity degree 4, the numbers of features are generated as 10, 25, 50 and 100. The other datasets have 5, 10, 25 and 50 features. As a result, 80 artificial datasets are generated by (4 different feature number * 4 different sample number * 5 different colinearity degree) The last attribute in each file is the target.

51 features

oz51 (target)numeric500 unique values
0 missing
oz1numeric500 unique values
0 missing
oz2numeric500 unique values
0 missing
oz3numeric500 unique values
0 missing
oz4numeric500 unique values
0 missing
oz5numeric500 unique values
0 missing
oz6numeric500 unique values
0 missing
oz7numeric500 unique values
0 missing
oz8numeric500 unique values
0 missing
oz9numeric500 unique values
0 missing
oz10numeric500 unique values
0 missing
oz11numeric500 unique values
0 missing
oz12numeric500 unique values
0 missing
oz13numeric500 unique values
0 missing
oz14numeric500 unique values
0 missing
oz15numeric500 unique values
0 missing
oz16numeric500 unique values
0 missing
oz17numeric500 unique values
0 missing
oz18numeric500 unique values
0 missing
oz19numeric500 unique values
0 missing
oz20numeric500 unique values
0 missing
oz21numeric500 unique values
0 missing
oz22numeric500 unique values
0 missing
oz23numeric500 unique values
0 missing
oz24numeric500 unique values
0 missing
oz25numeric500 unique values
0 missing
oz26numeric500 unique values
0 missing
oz27numeric500 unique values
0 missing
oz28numeric500 unique values
0 missing
oz29numeric500 unique values
0 missing
oz30numeric500 unique values
0 missing
oz31numeric500 unique values
0 missing
oz32numeric500 unique values
0 missing
oz33numeric500 unique values
0 missing
oz34numeric500 unique values
0 missing
oz35numeric500 unique values
0 missing
oz36numeric500 unique values
0 missing
oz37numeric500 unique values
0 missing
oz38numeric500 unique values
0 missing
oz39numeric500 unique values
0 missing
oz40numeric500 unique values
0 missing
oz41numeric500 unique values
0 missing
oz42numeric500 unique values
0 missing
oz43numeric500 unique values
0 missing
oz44numeric500 unique values
0 missing
oz45numeric500 unique values
0 missing
oz46numeric500 unique values
0 missing
oz47numeric500 unique values
0 missing
oz48numeric500 unique values
0 missing
oz49numeric500 unique values
0 missing
oz50numeric500 unique values
0 missing

19 properties

500
Number of instances (rows) of the dataset.
51
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.
51
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
-0.1
Average class difference between consecutive instances.
100
Percentage of numeric attributes.
0.1
Number of attributes divided by the number of instances.
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.

13 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz51
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz51
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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