Mercurial > repos > bgruening > keras_model_builder
view test-data/pipeline_params18 @ 15:20cd4269deb2 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
| author | bgruening |
|---|---|
| date | Wed, 09 Aug 2023 14:44:43 +0000 |
| parents | 449a757be9c9 |
| children |
line wrap: on
line source
Parameter Value * memory memory: None @ powertransformer powertransformer: PowerTransformer(copy=True, method='yeo-johnson', standardize=True) * steps "steps: [('powertransformer', PowerTransformer(copy=True, method='yeo-johnson', standardize=True)), ('transformedtargetregressor', TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None, regressor=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='warn', n_jobs=1, oob_score=False, random_state=10, verbose=0, warm_start=False), transformer=QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000, output_distribution='uniform', random_state=10, subsample=100000)))]" @ transformedtargetregressor "transformedtargetregressor: TransformedTargetRegressor(check_inverse=True, func=None, inverse_func=None, regressor=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='warn', n_jobs=1, oob_score=False, random_state=10, verbose=0, warm_start=False), transformer=QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000, output_distribution='uniform', random_state=10, subsample=100000))" @ verbose verbose: False @ powertransformer__copy powertransformer__copy: True @ powertransformer__method powertransformer__method: 'yeo-johnson' @ powertransformer__standardize powertransformer__standardize: True @ transformedtargetregressor__check_inverse transformedtargetregressor__check_inverse: True @ transformedtargetregressor__func transformedtargetregressor__func: None @ transformedtargetregressor__inverse_func transformedtargetregressor__inverse_func: None @ transformedtargetregressor__regressor "transformedtargetregressor__regressor: 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='warn', n_jobs=1, oob_score=False, random_state=10, verbose=0, warm_start=False)" @ transformedtargetregressor__regressor__bootstrap transformedtargetregressor__regressor__bootstrap: True @ transformedtargetregressor__regressor__criterion transformedtargetregressor__regressor__criterion: 'mse' @ transformedtargetregressor__regressor__max_depth transformedtargetregressor__regressor__max_depth: None @ transformedtargetregressor__regressor__max_features transformedtargetregressor__regressor__max_features: 'auto' @ transformedtargetregressor__regressor__max_leaf_nodes transformedtargetregressor__regressor__max_leaf_nodes: None @ transformedtargetregressor__regressor__min_impurity_decrease transformedtargetregressor__regressor__min_impurity_decrease: 0.0 @ transformedtargetregressor__regressor__min_impurity_split transformedtargetregressor__regressor__min_impurity_split: None @ transformedtargetregressor__regressor__min_samples_leaf transformedtargetregressor__regressor__min_samples_leaf: 1 @ transformedtargetregressor__regressor__min_samples_split transformedtargetregressor__regressor__min_samples_split: 2 @ transformedtargetregressor__regressor__min_weight_fraction_leaf transformedtargetregressor__regressor__min_weight_fraction_leaf: 0.0 @ transformedtargetregressor__regressor__n_estimators transformedtargetregressor__regressor__n_estimators: 'warn' * transformedtargetregressor__regressor__n_jobs transformedtargetregressor__regressor__n_jobs: 1 @ transformedtargetregressor__regressor__oob_score transformedtargetregressor__regressor__oob_score: False @ transformedtargetregressor__regressor__random_state transformedtargetregressor__regressor__random_state: 10 @ transformedtargetregressor__regressor__verbose transformedtargetregressor__regressor__verbose: 0 @ transformedtargetregressor__regressor__warm_start transformedtargetregressor__regressor__warm_start: False @ transformedtargetregressor__transformer "transformedtargetregressor__transformer: QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000, output_distribution='uniform', random_state=10, subsample=100000)" @ transformedtargetregressor__transformer__copy transformedtargetregressor__transformer__copy: True @ transformedtargetregressor__transformer__ignore_implicit_zeros transformedtargetregressor__transformer__ignore_implicit_zeros: False @ transformedtargetregressor__transformer__n_quantiles transformedtargetregressor__transformer__n_quantiles: 1000 @ transformedtargetregressor__transformer__output_distribution transformedtargetregressor__transformer__output_distribution: 'uniform' @ transformedtargetregressor__transformer__random_state transformedtargetregressor__transformer__random_state: 10 @ transformedtargetregressor__transformer__subsample transformedtargetregressor__transformer__subsample: 100000 Note: @, params eligible for search in searchcv tool.