Mercurial > repos > bgruening > sklearn_pca
diff test-data/get_params12.tabular @ 0:2d7016b3ae92 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5"
author | bgruening |
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date | Fri, 02 Oct 2020 08:45:21 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/get_params12.tabular Fri Oct 02 08:45:21 2020 +0000 @@ -0,0 +1,32 @@ + Parameter Value +@ estimator "estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, + colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, + max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, + n_jobs=1, nthread=None, objective='reg:linear', random_state=0, + reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, + silent=True, subsample=1)" +@ n_features_to_select n_features_to_select: None +* step step: 1 +* verbose verbose: 0 +@ estimator__base_score estimator__base_score: 0.5 +@ estimator__booster estimator__booster: 'gbtree' +@ estimator__colsample_bylevel estimator__colsample_bylevel: 1 +@ estimator__colsample_bytree estimator__colsample_bytree: 1 +@ estimator__gamma estimator__gamma: 0 +@ estimator__learning_rate estimator__learning_rate: 0.1 +@ estimator__max_delta_step estimator__max_delta_step: 0 +@ estimator__max_depth estimator__max_depth: 3 +@ estimator__min_child_weight estimator__min_child_weight: 1 +@ estimator__missing estimator__missing: nan +@ estimator__n_estimators estimator__n_estimators: 100 +* estimator__n_jobs estimator__n_jobs: 1 +* estimator__nthread estimator__nthread: None +@ estimator__objective estimator__objective: 'reg:linear' +@ estimator__random_state estimator__random_state: 0 +@ estimator__reg_alpha estimator__reg_alpha: 0 +@ estimator__reg_lambda estimator__reg_lambda: 1 +@ estimator__scale_pos_weight estimator__scale_pos_weight: 1 +@ estimator__seed estimator__seed: None +@ estimator__silent estimator__silent: True +@ estimator__subsample estimator__subsample: 1 + Note: @, params eligible for search in searchcv tool.