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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))

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))

Visibility: public Uploaded 13-03-2017 by Xiaolei Wang sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 4 runs
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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)

Parameters

cv		default: 10
error_score		default: "raise"
estimator		default: {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
fit_params
iid		default: true
n_jobs		default: -1
param_grid		default: {"Classifier__n_estimators": [8, 32, 128, 512]}
pre_dispatch		default: "2*n_jobs"
refit		default: true
return_train_score		default: true
scoring		default: "roc_auc"
verbose		default: 0

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