Mercurial > repos > bgruening > sklearn_train_test_eval

comparison test-data/get_params04.tabular @ 0:68aaa903052a draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
author bgruening
date Fri, 09 Aug 2019 07:09:06 -0400
parents
children
comparison
equal deleted inserted replaced
-1:000000000000 0:68aaa903052a
1 Parameter Value
2 * memory memory: None
3 * steps "steps: [('selectfrommodel', SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
4 learning_rate=1.0, n_estimators=50, random_state=None),
5 max_features=None, norm_order=1, prefit=False, threshold=None)), ('linearsvc', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
6 intercept_scaling=1, loss='squared_hinge', max_iter=1000,
7 multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
8 verbose=0))]"
9 @ selectfrommodel "selectfrommodel: SelectFromModel(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
10 learning_rate=1.0, n_estimators=50, random_state=None),
11 max_features=None, norm_order=1, prefit=False, threshold=None)"
12 @ linearsvc "linearsvc: LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
13 intercept_scaling=1, loss='squared_hinge', max_iter=1000,
14 multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
15 verbose=0)"
16 @ selectfrommodel__estimator__algorithm selectfrommodel__estimator__algorithm: 'SAMME.R'
17 @ selectfrommodel__estimator__base_estimator selectfrommodel__estimator__base_estimator: None
18 @ selectfrommodel__estimator__learning_rate selectfrommodel__estimator__learning_rate: 1.0
19 @ selectfrommodel__estimator__n_estimators selectfrommodel__estimator__n_estimators: 50
20 @ selectfrommodel__estimator__random_state selectfrommodel__estimator__random_state: None
21 @ selectfrommodel__estimator "selectfrommodel__estimator: AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
22 learning_rate=1.0, n_estimators=50, random_state=None)"
23 @ selectfrommodel__max_features selectfrommodel__max_features: None
24 @ selectfrommodel__norm_order selectfrommodel__norm_order: 1
25 @ selectfrommodel__prefit selectfrommodel__prefit: False
26 @ selectfrommodel__threshold selectfrommodel__threshold: None
27 @ linearsvc__C linearsvc__C: 1.0
28 @ linearsvc__class_weight linearsvc__class_weight: None
29 @ linearsvc__dual linearsvc__dual: True
30 @ linearsvc__fit_intercept linearsvc__fit_intercept: True
31 @ linearsvc__intercept_scaling linearsvc__intercept_scaling: 1
32 @ linearsvc__loss linearsvc__loss: 'squared_hinge'
33 @ linearsvc__max_iter linearsvc__max_iter: 1000
34 @ linearsvc__multi_class linearsvc__multi_class: 'ovr'
35 @ linearsvc__penalty linearsvc__penalty: 'l2'
36 @ linearsvc__random_state linearsvc__random_state: None
37 @ linearsvc__tol linearsvc__tol: 0.0001
38 * linearsvc__verbose linearsvc__verbose: 0
39 Note: @, searchable params in searchcv too.