Run
58207

Run 58207

Task 259 (Supervised Classification) collins Uploaded 13-04-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_7769
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Flow

sklearn.ensemble._forest.RandomForestClassifier(16)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(16)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(16)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(16)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(16)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(16)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(16)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(16)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(16)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(16)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(16)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(16)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(16)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(16)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(16)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(16)_random_state27916
sklearn.ensemble._forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(16)_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

0.9933 ± 0.0035
Per class
Cross-validation details (10-fold Crossvalidation)
0.8678 ± 0.0707
Per class
Cross-validation details (10-fold Crossvalidation)
0.865 ± 0.0561
Cross-validation details (10-fold Crossvalidation)
0.6534 ± 0.0229
Cross-validation details (10-fold Crossvalidation)
0.0718 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.878 ± 0.0503
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8836 ± 0.0579
Per class
Cross-validation details (10-fold Crossvalidation)
0.878 ± 0.0503
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5924 ± 0.0221
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1616 ± 0.0064
Cross-validation details (10-fold Crossvalidation)
0.6566 ± 0.0262
Cross-validation details (10-fold Crossvalidation)
0.7683 ± 0.0366
Cross-validation details (10-fold Crossvalidation)