Run
38510

Run 38510

Task 259 (Supervised Classification) collins Uploaded 07-04-2021 by Test Test
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  • openml-python Sklearn_0.24.0. study_5112
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Flow

sklearn.ensemble._forest.RandomForestClassifier(14)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(14)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(14)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(14)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(14)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(14)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(14)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(14)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(14)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(14)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(14)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(14)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(14)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(14)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(14)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(14)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(14)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(14)_random_state38507
sklearn.ensemble._forest.RandomForestClassifier(14)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(14)_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

0.9921 ± 0.0044
Per class
Cross-validation details (10-fold Crossvalidation)
0.8506 ± 0.0392
Per class
Cross-validation details (10-fold Crossvalidation)
0.845 ± 0.0567
Cross-validation details (10-fold Crossvalidation)
0.652 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
0.0715 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.86 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8678 ± 0.0374
Per class
Cross-validation details (10-fold Crossvalidation)
0.86 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5897 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1615 ± 0.006
Cross-validation details (10-fold Crossvalidation)
0.6562 ± 0.0248
Cross-validation details (10-fold Crossvalidation)
0.7488 ± 0.05
Cross-validation details (10-fold Crossvalidation)