Add train_deviance_path_ to GeneralizedLinearRegressorCV#982
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Matthias Schmidtblaicher (MatthiasSchmidtblaicherQC) merged 14 commits intomainfrom Mar 23, 2026
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Add train_deviance_path_ to GeneralizedLinearRegressorCV#982Matthias Schmidtblaicher (MatthiasSchmidtblaicherQC) merged 14 commits intomainfrom
train_deviance_path_ to GeneralizedLinearRegressorCV#982Matthias Schmidtblaicher (MatthiasSchmidtblaicherQC) merged 14 commits intomainfrom
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Expose training-set deviance alongside validation deviance so users can diagnose over-/under-fitting as regularization changes. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Pull request overview
This PR extends GeneralizedLinearRegressorCV to expose training-set deviance values along the CV regularization path, enabling direct comparison between training and validation performance across folds, l1_ratios, and alphas.
Changes:
- Add
train_deviance_path_computed within the existing CV loop inGeneralizedLinearRegressorCV. - Add a new test validating the presence and shape of
train_deviance_path_. - Add a
CHANGELOG.rstentry documenting the new attribute.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
src/glum/_glm_cv.py |
Computes and stores train_deviance_path_ alongside existing deviance_path_ and updates attribute docs. |
tests/glm/test_glm_cv.py |
Extends solver-equivalence assertions and adds a new test for train_deviance_path_. |
CHANGELOG.rst |
Documents the new train_deviance_path_ feature for the next release. |
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Matthias Schmidtblaicher (MatthiasSchmidtblaicherQC)
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March 10, 2026 18:05
Compare overall means instead of per-alpha to avoid failures from unlucky CV splits or high-regularization alphas. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Matthias Schmidtblaicher (MatthiasSchmidtblaicherQC)
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Marc-Antoine Schmidt (MarcAntoineSchmidtQC)
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Mar 23, 2026
Matthias Schmidtblaicher (MatthiasSchmidtblaicherQC)
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Addresses last point on #933 .
GeneralizedLinearRegressorCVnow exposestrain_deviance_path_alongside the existingdeviance_path_(validation deviance). This makes it easy to compare training and validation performance across the regularization path.train_deviance_path_with shape(n_folds, n_l1_ratios, n_alphas),matching deviance_path_.x_trainbefore standardization so train deviance is evaluated on the original scale.Checklist
CHANGELOG.rstentry