Run
42425

Run 42425

Task 259 (Supervised Classification) collins Uploaded 08-04-2021 by Test Test
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  • openml-python Sklearn_0.24.0. study_5629
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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_state4924
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

0.9934 ± 0.0051
Per class
Cross-validation details (10-fold Crossvalidation)
0.8703 ± 0.0027
Per class
Cross-validation details (10-fold Crossvalidation)
0.8629 ± 0.0501
Cross-validation details (10-fold Crossvalidation)
0.655 ± 0.0214
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)
0.876 ± 0.045
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8731 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
0.876 ± 0.045
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.59 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1611 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
0.6548 ± 0.0231
Cross-validation details (10-fold Crossvalidation)
0.7786 ± 0.0252
Cross-validation details (10-fold Crossvalidation)