Allows disabling refitting of CV estimators #30463
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I think I'm done from my side with the code changes and documentation. Code changes for |
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@kayo09, please do not make such reviews - it confuses us. Only @paulAdrienMarie and I are assigned to work on this PR. Edit: @kayo09, could you try to remove the review. |
Update _coordinate_descent.py
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@paulAdrienMarie The refit parameter was supposed to appear after cv parameter. This is not a programming issue, but a conflict with the sklearn docs automation, as I followed the scheme followed by GridSearchCV and accordingly made the documentation in the same order, ie refit after cv.
Just sharing this as feedback so that it may be useful for you in the future - not as criticism.
This was the error message from which I understood:
AssertionError: Docstring Error:
In function: sklearn.linear_model._coordinate_descent.ElasticNetCV.__init__
There's a parameter name mismatch in function docstring w.r.t. function signature, at index 8 diff: 'cv' != 'refit'
Full diff:
['l1_ratio',
'eps',
'n_alphas',
'alphas',
'fit_intercept',
'precompute',
'max_iter',
'tol',
+ 'refit',
'cv',
'copy_X',
- 'refit',
'verbose',
'n_jobs',
'positive',
'random_state',
'selection']
In function: sklearn.linear_model._coordinate_descent.MultiTaskElasticNetCV.__init__
There's a parameter name mismatch in function docstring w.r.t. function signature, at index 7 diff: 'cv' != 'refit'
Full diff:
['l1_ratio',
'eps',
'n_alphas',
'alphas',
'fit_intercept',
'max_iter',
'tol',
+ 'refit',
'cv',
'copy_X',
- 'refit',
'verbose',
'n_jobs',
'random_state',
'selection']
Reference Issues/PRs
What does this implement/fix? Explain your changes.
What
refitto toggle this behavior.Why
User does not want to waste resources on refitting when user only wants one/more of the following, which do not involve refitting:
This is especially impactful for large datasets.
How
refitargument which disables refitting ifrefit=False.refit=Trueis set to prevent breaking existing usageAny other comments?
This is my first (hopefully first of many) PR for scikit-learn. If you have any feedback on my implementation/PR documentation/etc, feel free to share - I'd really appreciate it.
Thanks to @paulAdrienMarie for all the support!