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fri_c4_500_100

fri_c4_500_100

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.

101 features

oz101 (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
oz51numeric500 unique values
0 missing
oz52numeric500 unique values
0 missing
oz53numeric500 unique values
0 missing
oz54numeric500 unique values
0 missing
oz55numeric500 unique values
0 missing
oz56numeric500 unique values
0 missing
oz57numeric500 unique values
0 missing
oz58numeric500 unique values
0 missing
oz59numeric500 unique values
0 missing
oz60numeric500 unique values
0 missing
oz61numeric500 unique values
0 missing
oz62numeric500 unique values
0 missing
oz63numeric500 unique values
0 missing
oz64numeric500 unique values
0 missing
oz65numeric500 unique values
0 missing
oz66numeric500 unique values
0 missing
oz67numeric500 unique values
0 missing
oz68numeric500 unique values
0 missing
oz69numeric500 unique values
0 missing
oz70numeric500 unique values
0 missing
oz71numeric500 unique values
0 missing
oz72numeric500 unique values
0 missing
oz73numeric500 unique values
0 missing
oz74numeric500 unique values
0 missing
oz75numeric500 unique values
0 missing
oz76numeric500 unique values
0 missing
oz77numeric500 unique values
0 missing
oz78numeric500 unique values
0 missing
oz79numeric500 unique values
0 missing
oz80numeric500 unique values
0 missing
oz81numeric500 unique values
0 missing
oz82numeric500 unique values
0 missing
oz83numeric500 unique values
0 missing
oz84numeric500 unique values
0 missing
oz85numeric500 unique values
0 missing
oz86numeric500 unique values
0 missing
oz87numeric500 unique values
0 missing
oz88numeric500 unique values
0 missing
oz89numeric500 unique values
0 missing
oz90numeric500 unique values
0 missing
oz91numeric500 unique values
0 missing
oz92numeric500 unique values
0 missing
oz93numeric500 unique values
0 missing
oz94numeric500 unique values
0 missing
oz95numeric500 unique values
0 missing
oz96numeric500 unique values
0 missing
oz97numeric500 unique values
0 missing
oz98numeric500 unique values
0 missing
oz99numeric500 unique values
0 missing
oz100numeric500 unique values
0 missing

107 properties

500
Number of instances (rows) of the dataset.
101
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.
101
Number of numeric attributes.
0
Number of nominal attributes.
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric attributes.
0
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
1
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
0
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.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.06
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
-1.11
Mean kurtosis among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.23
First quartile of kurtosis among attributes of the numeric type.
1
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
0
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
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.
0
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
Average number of distinct values among the attributes of the nominal type.
-0.04
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
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
0.03
Mean skewness among attributes of the numeric type.
1
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
1
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.
-1.33
Minimum kurtosis among attributes of the numeric type.
-0
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.
-0
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
1.69
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.01
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
0
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
1
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
0.2
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.22
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.
The maximum number of distinct values among attributes of the nominal type.
1
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-1.16
Third quartile of kurtosis among attributes of the numeric type.
-0.11
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
1.27
Maximum skewness among attributes of the numeric type.

14 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz101
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz101
0 runs - estimation_procedure: 33% Holdout set - target_feature: oz101
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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