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Task 145677 (Supervised Classification) Bioresponse Uploaded 15-03-2017 by Xiaolei Wang
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pip eline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.featu re_selection.univariate_selection.SelectPercentile,scaler=sklearn.preproces sing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClass ifier))(1)	Automatically created sub-component.
sklearn.ensemble.forest.RandomForestClassifier(16)_bootstrap	True
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weight	None
sklearn.ensemble.forest.RandomForestClassifier(16)_criterion	gini
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depth	None
sklearn.ensemble.forest.RandomForestClassifier(16)_max_features	auto
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodes	None
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_split	1e-07
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf	1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split	2
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf	0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators	10
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobs	-1
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_score	False
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state	None
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_start	False
sklearn.preprocessing.imputation.Imputer(3)_axis	0
sklearn.preprocessing.imputation.Imputer(3)_copy	True
sklearn.preprocessing.imputation.Imputer(3)_missing_values	NaN
sklearn.preprocessing.imputation.Imputer(3)_strategy	mean
sklearn.preprocessing.imputation.Imputer(3)_verbose	0
sklearn.feature_selection.univariate_selection.SelectPercentile(1)_percentile	5
sklearn.feature_selection.univariate_selection.SelectPercentile(1)_score_func
sklearn.preprocessing.data.MinMaxScaler(1)_copy	True
sklearn.preprocessing.data.MinMaxScaler(1)_feature_range	(0, 1)
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_cv	10
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_error_score	raise
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_estimator	Pipeline(steps=[('Imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)), ('select', SelectPercentile(percentile=5, score_func=)), ('scaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('Classifier', RandomForestClassifier(b...mators=10, n_jobs=-1, oob_score=False, random_state=None, verbose=0, warm_start=False))])
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_fit_params	{}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_iid	True
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_n_jobs	-1
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_param_grid	{'Classifier__max_features': [1, 3, 5], 'Classifier__n_estimators': [32, 128, 1024]}
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_pre_dispatch	2*n_jobs
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_refit	True
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_return_train_score	True
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_scoring	roc_auc
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)_verbose	0
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps	[('Imputer', Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)), ('select', SelectPercentile(percentile=5, score_func=)), ('scaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('Classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1, oob_score=False, random_state=None, verbose=0, warm_start=False))]