| sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_estimator | ExtraTreesClassifier(bootstrap=False, 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) |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),svc=sklearn.svm.classes.SVC)(1)_steps | [('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('selectfrommodel', SelectFromModel(estimator=ExtraTreesClassifier(bootstrap=False, 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),
prefit=False, threshold=None)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False))] |