Parameter Value * memory memory: None * steps "steps: [('editednearestneighbours', EditedNearestNeighbours(kind_sel='all', n_jobs=1, n_neighbors=3, random_state=None, ratio=None, return_indices=False, sampling_strategy='auto')), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False))]" @ editednearestneighbours "editednearestneighbours: EditedNearestNeighbours(kind_sel='all', n_jobs=1, n_neighbors=3, random_state=None, ratio=None, return_indices=False, sampling_strategy='auto')" @ randomforestclassifier "randomforestclassifier: RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False)" @ editednearestneighbours__kind_sel editednearestneighbours__kind_sel: 'all' * editednearestneighbours__n_jobs editednearestneighbours__n_jobs: 1 @ editednearestneighbours__n_neighbors editednearestneighbours__n_neighbors: 3 @ editednearestneighbours__random_state editednearestneighbours__random_state: None @ editednearestneighbours__ratio editednearestneighbours__ratio: None @ editednearestneighbours__return_indices editednearestneighbours__return_indices: False @ editednearestneighbours__sampling_strategy editednearestneighbours__sampling_strategy: 'auto' @ randomforestclassifier__bootstrap randomforestclassifier__bootstrap: True @ randomforestclassifier__class_weight randomforestclassifier__class_weight: None @ randomforestclassifier__criterion randomforestclassifier__criterion: 'gini' @ randomforestclassifier__max_depth randomforestclassifier__max_depth: None @ randomforestclassifier__max_features randomforestclassifier__max_features: 'auto' @ randomforestclassifier__max_leaf_nodes randomforestclassifier__max_leaf_nodes: None @ randomforestclassifier__min_impurity_decrease randomforestclassifier__min_impurity_decrease: 0.0 @ randomforestclassifier__min_impurity_split randomforestclassifier__min_impurity_split: None @ randomforestclassifier__min_samples_leaf randomforestclassifier__min_samples_leaf: 1 @ randomforestclassifier__min_samples_split randomforestclassifier__min_samples_split: 2 @ randomforestclassifier__min_weight_fraction_leaf randomforestclassifier__min_weight_fraction_leaf: 0.0 @ randomforestclassifier__n_estimators randomforestclassifier__n_estimators: 'warn' * randomforestclassifier__n_jobs randomforestclassifier__n_jobs: 1 @ randomforestclassifier__oob_score randomforestclassifier__oob_score: False @ randomforestclassifier__random_state randomforestclassifier__random_state: None * randomforestclassifier__verbose randomforestclassifier__verbose: 0 @ randomforestclassifier__warm_start randomforestclassifier__warm_start: False Note: @, searchable params in searchcv too.