OpenML
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(steps__feature_selection=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__scaler=sklearn.preprocessing.data.MinMaxScaler,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier))

sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(steps__feature_selection=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__scaler=sklearn.preprocessing.data.MinMaxScaler,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier))

Visibility: public Uploaded 17-03-2017 by Corbin Joosen sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9 0 runs
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Automatically created sub-component.

Components

Parameters

cvdefault: 3
error_scoredefault: "raise"
estimatordefault: {"oml-python:serialized_object": "component_reference", "value": {"step_name": null, "key": "estimator"}}
fit_params
iiddefault: true
n_jobsdefault: -1
param_griddefault: {"classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], "classifier__n_estimators": [1, 2, 4, 8, 16, 32, 64, 128]}
pre_dispatchdefault: "2*n_jobs"
refitdefault: true
return_train_scoredefault: true
scoringdefault: "roc_auc"
verbosedefault: 0

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