Run 10706

Task 119 (Supervised Classification) diabetes Uploaded 29-11-2022 by Test Test
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sklearn.ensemble._forest.RandomForestClassifier(9)	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(9)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(9)_ccp_alpha	0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(9)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_depth	null
sklearn.ensemble._forest.RandomForestClassifier(9)_max_features	"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(9)_max_leaf_nodes	null
sklearn.ensemble._forest.RandomForestClassifier(9)_max_samples	null
sklearn.ensemble._forest.RandomForestClassifier(9)_min_impurity_decrease	0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_leaf	1
sklearn.ensemble._forest.RandomForestClassifier(9)_min_samples_split	2
sklearn.ensemble._forest.RandomForestClassifier(9)_min_weight_fraction_leaf	0.0
sklearn.ensemble._forest.RandomForestClassifier(9)_n_estimators	100
sklearn.ensemble._forest.RandomForestClassifier(9)_n_jobs	null
sklearn.ensemble._forest.RandomForestClassifier(9)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(9)_random_state	50005
sklearn.ensemble._forest.RandomForestClassifier(9)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(9)_warm_start	false