Mercurial > repos > bgruening > sklearn_train_test_split

comparison test-data/get_params12.tabular @ 2:5a092779412e draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author bgruening
date Mon, 16 Dec 2019 05:34:17 -0500
parents 0985b0dd6f1a
children
comparison
equal deleted inserted replaced
1:910ebff96ddc 2:5a092779412e
1 Parameter Value 1 Parameter Value
2 * memory memory: None 2 @ estimator "estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
3 * steps "steps: [('rfe', RFE(estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
4 colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
5 max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
6 n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
7 reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
8 silent=True, subsample=1),
9 n_features_to_select=None, step=1, verbose=0))]"
10 @ rfe "rfe: RFE(estimator=XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
11 colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
12 max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
13 n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
14 reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
15 silent=True, subsample=1),
16 n_features_to_select=None, step=1, verbose=0)"
17 @ rfe__estimator__base_score rfe__estimator__base_score: 0.5
18 @ rfe__estimator__booster rfe__estimator__booster: 'gbtree'
19 @ rfe__estimator__colsample_bylevel rfe__estimator__colsample_bylevel: 1
20 @ rfe__estimator__colsample_bytree rfe__estimator__colsample_bytree: 1
21 @ rfe__estimator__gamma rfe__estimator__gamma: 0
22 @ rfe__estimator__learning_rate rfe__estimator__learning_rate: 0.1
23 @ rfe__estimator__max_delta_step rfe__estimator__max_delta_step: 0
24 @ rfe__estimator__max_depth rfe__estimator__max_depth: 3
25 @ rfe__estimator__min_child_weight rfe__estimator__min_child_weight: 1
26 @ rfe__estimator__missing rfe__estimator__missing: nan
27 @ rfe__estimator__n_estimators rfe__estimator__n_estimators: 100
28 * rfe__estimator__n_jobs rfe__estimator__n_jobs: 1
29 * rfe__estimator__nthread rfe__estimator__nthread: None
30 @ rfe__estimator__objective rfe__estimator__objective: 'reg:linear'
31 @ rfe__estimator__random_state rfe__estimator__random_state: 0
32 @ rfe__estimator__reg_alpha rfe__estimator__reg_alpha: 0
33 @ rfe__estimator__reg_lambda rfe__estimator__reg_lambda: 1
34 @ rfe__estimator__scale_pos_weight rfe__estimator__scale_pos_weight: 1
35 @ rfe__estimator__seed rfe__estimator__seed: None
36 @ rfe__estimator__silent rfe__estimator__silent: True
37 @ rfe__estimator__subsample rfe__estimator__subsample: 1
38 @ rfe__estimator "rfe__estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
39 colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, 3 colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
40 max_depth=3, min_child_weight=1, missing=nan, n_estimators=100, 4 max_depth=3, min_child_weight=1, missing=nan, n_estimators=100,
41 n_jobs=1, nthread=None, objective='reg:linear', random_state=0, 5 n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
42 reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, 6 reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
43 silent=True, subsample=1)" 7 silent=True, subsample=1)"
44 @ rfe__n_features_to_select rfe__n_features_to_select: None 8 @ n_features_to_select n_features_to_select: None
45 @ rfe__step rfe__step: 1 9 * step step: 1
46 * rfe__verbose rfe__verbose: 0 10 * verbose verbose: 0
47 Note: @, searchable params in searchcv too. 11 @ estimator__base_score estimator__base_score: 0.5
12 @ estimator__booster estimator__booster: 'gbtree'
13 @ estimator__colsample_bylevel estimator__colsample_bylevel: 1
14 @ estimator__colsample_bytree estimator__colsample_bytree: 1
15 @ estimator__gamma estimator__gamma: 0
16 @ estimator__learning_rate estimator__learning_rate: 0.1
17 @ estimator__max_delta_step estimator__max_delta_step: 0
18 @ estimator__max_depth estimator__max_depth: 3
19 @ estimator__min_child_weight estimator__min_child_weight: 1
20 @ estimator__missing estimator__missing: nan
21 @ estimator__n_estimators estimator__n_estimators: 100
22 * estimator__n_jobs estimator__n_jobs: 1
23 * estimator__nthread estimator__nthread: None
24 @ estimator__objective estimator__objective: 'reg:linear'
25 @ estimator__random_state estimator__random_state: 0
26 @ estimator__reg_alpha estimator__reg_alpha: 0
27 @ estimator__reg_lambda estimator__reg_lambda: 1
28 @ estimator__scale_pos_weight estimator__scale_pos_weight: 1
29 @ estimator__seed estimator__seed: None
30 @ estimator__silent estimator__silent: True
31 @ estimator__subsample estimator__subsample: 1
32 Note: @, params eligible for search in searchcv tool.