Run
52840

Run 52840

Task 259 (Supervised Classification) collins Uploaded 09-04-2021 by Test Test
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  • openml-python Sklearn_0.24.1. study_7024
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Flow

sklearn.ensemble._forest.RandomForestClassifier(16)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(16)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(16)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(16)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(16)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(16)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(16)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(16)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(16)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(16)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(16)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(16)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(16)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(16)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(16)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(16)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(16)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(16)_random_state14714
sklearn.ensemble._forest.RandomForestClassifier(16)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(16)_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

0.9917 ± 0.0041
Per class
Cross-validation details (10-fold Crossvalidation)
0.8608 ± 0.0944
Per class
Cross-validation details (10-fold Crossvalidation)
0.8563 ± 0.0647
Cross-validation details (10-fold Crossvalidation)
0.6513 ± 0.0243
Cross-validation details (10-fold Crossvalidation)
0.0715 ± 0.003
Cross-validation details (10-fold Crossvalidation)
0.1212 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.87 ± 0.0583
Cross-validation details (10-fold Crossvalidation)
500
Per class
Cross-validation details (10-fold Crossvalidation)
0.8731 ± 0.061
Per class
Cross-validation details (10-fold Crossvalidation)
0.87 ± 0.0583
Cross-validation details (10-fold Crossvalidation)
3.6489 ± 0.0337
Cross-validation details (10-fold Crossvalidation)
0.5902 ± 0.0246
Cross-validation details (10-fold Crossvalidation)
0.246 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1614 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
0.6559 ± 0.0274
Cross-validation details (10-fold Crossvalidation)
0.7681 ± 0.0911
Cross-validation details (10-fold Crossvalidation)