Run 9099

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklear n.ensemble.forest.RandomForestClassifier)(1)	Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_cv	{"oml-python:serialized_object": "cv_object", "value": {"name": "sklearn.model_selection._split.StratifiedKFold", "parameters": {"n_splits": "2", "random_state": "62501", "shuffle": "true"}}}
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_error_score	"raise"
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_fit_params	{}
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_iid	true
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobs	1
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_param_grid	[{"max_features": [2, 4]}, {"min_samples_leaf": [1, 10]}]
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_pre_dispatch	"2*n_jobs"
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_refit	true
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_return_train_score	true
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_scoring	null
TEST6c946ab1absklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose	0
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_bootstrap	true
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_class_weight	null
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_criterion	"gini"
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_max_depth	null
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_max_features	"auto"
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_max_leaf_nodes	null
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_min_impurity_split	1e-07
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_leaf	1
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_min_samples_split	2
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_min_weight_fraction_leaf	0.0
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_n_estimators	5
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_n_jobs	1
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_oob_score	false
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_random_state	33003
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_verbose	0
TEST6c946ab1absklearn.ensemble.forest.RandomForestClassifier(1)_warm_start	false