| 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.tree.tree.DecisionTreeClassifier))(1)_steps | [('Imputer', ConditionalImputer(axis=0, categorical_features=[0, 78, 79, 80], copy=True,
empty_attribute_constant=0, missing_values='NaN',
strategy='median', strategy_nominal='most_frequent', verbose=0)), ('OneHotEncoder', OneHotEncoder(categorical_features=[0, 78, 79, 80],
dtype=, handle_unknown='ignore',
n_values='auto', sparse=False)), ('VarianceThreshold', VarianceThreshold(threshold=0.0)), ('Estimator', GridSearchCV(cv=3, error_score='raise',
estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=None, splitter='best'),
fit_params={}, iid=True, n_jobs=1,
param_grid={'min_samples_split': [2, 4, 8, 16, 32, 64, 128], 'min_samples_leaf': [1, 2, 4, 8, 16, 32, 64]},
pre_dispatch='2*n_job |
| sklearn.model_selection._search.GridSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_estimator | DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=None, splitter='best') |