Mercurial > repos > bgruening > sklearn_stacking_ensemble_models
view test-data/get_params05.tabular @ 0:fcc5eaaec401 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
author | bgruening |
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date | Wed, 15 May 2019 07:25:29 -0400 |
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children | a2e4a45c6083 |
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Parameter Value * memory memory: None * steps "steps: [('randomforestregressor', RandomForestRegressor(bootstrap=True, criterion='mse', 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=100, n_jobs=1, oob_score=False, random_state=42, verbose=0, warm_start=False))]" @ randomforestregressor "randomforestregressor: RandomForestRegressor(bootstrap=True, criterion='mse', 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=100, n_jobs=1, oob_score=False, random_state=42, verbose=0, warm_start=False)" @ randomforestregressor__bootstrap randomforestregressor__bootstrap: True @ randomforestregressor__criterion randomforestregressor__criterion: 'mse' @ randomforestregressor__max_depth randomforestregressor__max_depth: None @ randomforestregressor__max_features randomforestregressor__max_features: 'auto' @ randomforestregressor__max_leaf_nodes randomforestregressor__max_leaf_nodes: None @ randomforestregressor__min_impurity_decrease randomforestregressor__min_impurity_decrease: 0.0 @ randomforestregressor__min_impurity_split randomforestregressor__min_impurity_split: None @ randomforestregressor__min_samples_leaf randomforestregressor__min_samples_leaf: 1 @ randomforestregressor__min_samples_split randomforestregressor__min_samples_split: 2 @ randomforestregressor__min_weight_fraction_leaf randomforestregressor__min_weight_fraction_leaf: 0.0 @ randomforestregressor__n_estimators randomforestregressor__n_estimators: 100 * randomforestregressor__n_jobs randomforestregressor__n_jobs: 1 @ randomforestregressor__oob_score randomforestregressor__oob_score: False @ randomforestregressor__random_state randomforestregressor__random_state: 42 * randomforestregressor__verbose randomforestregressor__verbose: 0 @ randomforestregressor__warm_start randomforestregressor__warm_start: False Note: @, searchable params in searchcv too.