Run
45309

Run 45309

Task 259 (Supervised Classification) collins Uploaded 09-04-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_6019
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Flow

sklearn.ensemble._forest.RandomForestClassifier(11)A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the `max_samples` parameter if `bootstrap=True` (default), otherwise the whole dataset is used to build each tree.
sklearn.ensemble._forest.RandomForestClassifier(11)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(11)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(11)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(11)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(11)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(11)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(11)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(11)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(11)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(11)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(11)_random_state59863
sklearn.ensemble._forest.RandomForestClassifier(11)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(11)_warm_startfalse

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

0.9941 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
0.8663 ± 0.0009
Per class
Cross-validation details (10-fold Crossvalidation)
0.863 ± 0.0601
Cross-validation details (10-fold Crossvalidation)
0.6619 ± 0.0183
Cross-validation details (10-fold Crossvalidation)
0.0705 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.876 ± 0.054
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8735 ± 0.0143
Per class
Cross-validation details (10-fold Crossvalidation)
0.876 ± 0.054
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5816 ± 0.0195
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1592 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
0.6469 ± 0.0222
Cross-validation details (10-fold Crossvalidation)
0.7649 ± 0.0665
Cross-validation details (10-fold Crossvalidation)