Run
3680

Run 3680

Task 259 (Supervised Classification) collins Uploaded 01-03-2021 by Test Test
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  • openml-python Sklearn_0.23.1. study_490
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Flow

sklearn.ensemble._forest.RandomForestClassifier(2)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(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state7556
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_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

0.9931 ± 0.0035
Per class
Cross-validation details (10-fold Crossvalidation)
0.8511 ± 0.0385
Per class
Cross-validation details (10-fold Crossvalidation)
0.845 ± 0.0595
Cross-validation details (10-fold Crossvalidation)
0.6595 ± 0.0246
Cross-validation details (10-fold Crossvalidation)
0.0706 ± 0.003
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.86 ± 0.0533
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8602 ± 0.0305
Per class
Cross-validation details (10-fold Crossvalidation)
0.86 ± 0.0533
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5829 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1598 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
0.6494 ± 0.0256
Cross-validation details (10-fold Crossvalidation)
0.7546 ± 0.0274
Cross-validation details (10-fold Crossvalidation)