| sklearn.pipeline.Pipeline(Imputer=openml.utils.preprocessing.ConditionalImputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier))(1)_steps | [('Imputer', ConditionalImputer(axis=0, categorical_features=[], copy=True,
empty_attribute_constant=0, missing_values='NaN',
strategy='median', strategy_nominal='most_frequent', verbose=0)), ('OneHotEncoder', OneHotEncoder(categorical_features=[], dtype=,
handle_unknown='ignore', n_values='auto', sparse=False)), ('VarianceThreshold', VarianceThreshold(threshold=0.0)), ('Estimator', GridSearchCV(cv=3, error_score='raise',
estimator=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=500, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False),
fit_params={}, iid=True, n_jobs=1,
param_grid={'max_features': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},
pre_dispatch=' |
| sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)(2)_estimator | 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=500, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False) |