Run
64314

Run 64314

Task 259 (Supervised Classification) collins Uploaded 16-04-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_8581
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Flow

sklearn.ensemble._forest.RandomForestClassifier(20)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(20)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(20)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(20)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(20)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(20)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(20)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(20)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(20)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(20)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(20)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(20)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(20)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(20)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(20)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(20)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(20)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(20)_random_state47406
sklearn.ensemble._forest.RandomForestClassifier(20)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(20)_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.

16 Evaluation measures

0.9928 ± 0.0034
Per class
Cross-validation details (10-fold Crossvalidation)
0.8541 ± 0.0592
Cross-validation details (10-fold Crossvalidation)
0.6566 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
0.0707 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.868 ± 0.0535
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.868 ± 0.0535
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5838 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.16 ± 0.0064
Cross-validation details (10-fold Crossvalidation)
0.6501 ± 0.026
Cross-validation details (10-fold Crossvalidation)
0.7595 ± 0.0613
Cross-validation details (10-fold Crossvalidation)