Run
3681

Run 3681

Task 307 (Supervised Classification) kc2 Uploaded 01-03-2021 by Test Test
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  • openml-python Sklearn_0.23.1. study_490
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Flow

sklearn.ensemble._forest.RandomForestClassifier(2)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(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state7556
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_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

0.8108 ± 0.1032
Per class
Cross-validation details (10-fold Crossvalidation)
0.8254 ± 0.0451
Per class
Cross-validation details (10-fold Crossvalidation)
0.4358 ± 0.1352
Cross-validation details (10-fold Crossvalidation)
0.2483 ± 0.1436
Cross-validation details (10-fold Crossvalidation)
0.2196 ± 0.0342
Cross-validation details (10-fold Crossvalidation)
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
0.8372 ± 0.0435
Cross-validation details (10-fold Crossvalidation)
522
Per class
Cross-validation details (10-fold Crossvalidation)
0.8233 ± 0.0529
Per class
Cross-validation details (10-fold Crossvalidation)
0.8372 ± 0.0435
Cross-validation details (10-fold Crossvalidation)
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
0.6723 ± 0.1023
Cross-validation details (10-fold Crossvalidation)
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
0.3467 ± 0.0468
Cross-validation details (10-fold Crossvalidation)
0.8589 ± 0.111
Cross-validation details (10-fold Crossvalidation)
0.693 ± 0.0607
Cross-validation details (10-fold Crossvalidation)