Run
5653

Run 5653

Task 259 (Supervised Classification) collins Uploaded 02-03-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_755
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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_state15711
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

0.9937 ± 0.0036
Per class
Cross-validation details (10-fold Crossvalidation)
0.8719 ± 0.0507
Per class
Cross-validation details (10-fold Crossvalidation)
0.8652 ± 0.057
Cross-validation details (10-fold Crossvalidation)
0.6662 ± 0.0201
Cross-validation details (10-fold Crossvalidation)
0.0696 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.878 ± 0.0512
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8753 ± 0.0448
Per class
Cross-validation details (10-fold Crossvalidation)
0.878 ± 0.0512
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5743 ± 0.0207
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1581 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
0.6425 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
0.7809 ± 0.0623
Cross-validation details (10-fold Crossvalidation)