Run
8862

Run 8862

Task 307 (Supervised Classification) kc2 Uploaded 04-03-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_1167
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Flow

sklearn.ensemble._forest.RandomForestClassifier(1)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(1)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(1)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(1)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(1)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(1)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(1)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(1)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(1)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(1)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(1)_random_state43979
sklearn.ensemble._forest.RandomForestClassifier(1)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(1)_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

0.8068 ± 0.104
Per class
Cross-validation details (10-fold Crossvalidation)
0.8247 ± 0.0451
Per class
Cross-validation details (10-fold Crossvalidation)
0.4358 ± 0.1373
Cross-validation details (10-fold Crossvalidation)
0.2363 ± 0.1501
Cross-validation details (10-fold Crossvalidation)
0.222 ± 0.0351
Cross-validation details (10-fold Crossvalidation)
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
0.8352 ± 0.0441
Cross-validation details (10-fold Crossvalidation)
522
Per class
Cross-validation details (10-fold Crossvalidation)
0.8218 ± 0.0504
Per class
Cross-validation details (10-fold Crossvalidation)
0.8352 ± 0.0441
Cross-validation details (10-fold Crossvalidation)
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
0.6797 ± 0.1055
Cross-validation details (10-fold Crossvalidation)
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
0.3498 ± 0.0464
Cross-validation details (10-fold Crossvalidation)
0.8665 ± 0.1102
Cross-validation details (10-fold Crossvalidation)
0.6952 ± 0.0696
Cross-validation details (10-fold Crossvalidation)