Run
57186

Run 57186

Task 259 (Supervised Classification) collins Uploaded 12-04-2021 by Test Test
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  • openml-python Sklearn_0.24.0. study_7627
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Flow

sklearn.ensemble._forest.RandomForestClassifier(18)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(18)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(18)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(18)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(18)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(18)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(18)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(18)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(18)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(18)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(18)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(18)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(18)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(18)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(18)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(18)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(18)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(18)_random_state27761
sklearn.ensemble._forest.RandomForestClassifier(18)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(18)_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

0.9925 ± 0.0045
Per class
Cross-validation details (10-fold Crossvalidation)
0.8719 ± 0.0124
Per class
Cross-validation details (10-fold Crossvalidation)
0.8673 ± 0.0434
Cross-validation details (10-fold Crossvalidation)
0.6625 ± 0.026
Cross-validation details (10-fold Crossvalidation)
0.0699 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.88 ± 0.0389
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8817 ± 0.0147
Per class
Cross-validation details (10-fold Crossvalidation)
0.88 ± 0.0389
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5769 ± 0.0265
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1585 ± 0.0066
Cross-validation details (10-fold Crossvalidation)
0.6442 ± 0.027
Cross-validation details (10-fold Crossvalidation)
0.7802 ± 0.0431
Cross-validation details (10-fold Crossvalidation)