FIX Sample weight in BayesianRidge #30644
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Thanks for the PR. It is marked draft, but I have the feeling it's ready for review. Is there anything left to do before reviewing? |
ogrisel
reviewed
Jan 15, 2025
sklearn/linear_model/_bayes.py
Outdated
| @@ -394,12 +412,12 @@ def _update_coef_( | |||
| [X.T, U / (eigen_vals_ + lambda_ / alpha_)[None, :], U.T, y] | |||
| ) | |||
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| rmse_ = np.sum((y - np.dot(X, coef_)) ** 2) | |||
| mse_ = np.sum((y - np.dot(X, coef_)) ** 2) | |||
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Don't we need to compute the weighted mse here as well?
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It's computing the weighted mse because the X and y are scaled by sample_weight in _rescale_data
I think it's now ready for review. |
54 tasks
ogrisel
approved these changes
Jan 16, 2025
Co-authored-by: Olivier Grisel <[email protected]>
OmarManzoor
approved these changes
Jan 17, 2025
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Reference Issues/PRs
Part of meta issue #16298
What does this implement/fix? Explain your changes.
Some of the code in
BayesianRidgedid not handlesample_weightproperly, in particular the initialization and update for thealpha_hyperparameter. NowBayesianRidgepasses thecheck_sample_weight_equivalencetest.Any other comments?
Renaming
rmse -> sseas it is actually the sum of squared errors.