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Task 259 (Supervised Classification) collins Uploaded 03-07-2024 by Test Test
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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. 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(2)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	100
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state	36250
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_start	false