Run
9254

Run 9254

Task 307 (Supervised Classification) kc2 Uploaded 18-03-2024 by Test Test
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  • openml-python Sklearn_1.3.2. study_782
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Flow

sklearn.ensemble._forest.RandomForestClassifier(7)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. For a comparison between tree-based ensemble models see the example :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.
sklearn.ensemble._forest.RandomForestClassifier(7)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(7)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(7)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(7)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(7)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(7)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(7)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(7)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(7)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(7)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(7)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(7)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(7)_random_state4187
sklearn.ensemble._forest.RandomForestClassifier(7)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(7)_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

0.8072 ± 0.0947
Per class
0.8279 ± 0.05
Per class
0.4446 ± 0.1502
0.2441 ± 0.1423
0.2191 ± 0.033
0.3266 ± 0.0052
0.8391 ± 0.0497
522
Per class
0.8258 ± 0.0566
Per class
0.8391 ± 0.0497
0.7318 ± 0.0173
0.6709 ± 0.0994
0.4037 ± 0.0065
0.3486 ± 0.0443
0.8635 ± 0.1053
0.6976 ± 0.0674