FIX Covariance matrix in BayesianRidge #31094
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The failing tests on the Linux debian_32bit seem unrelated to this PR, so I think it is ready for review. FAILED tests/test_common.py::test_estimators[LinearRegression(positive=True)-check_sample_weight_equivalence_on_dense_data] - AssertionError:
FAILED utils/tests/test_estimator_checks.py::test_check_estimator_clones - AssertionError: |
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I opened #31098 for the CI failure. |
ogrisel
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Mar 28, 2025
doc/whats_new/upcoming_changes/sklearn.linear_model/31094.fix.rst
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Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
test_bayesian_covariance_matrix
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Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <jeremie@probabl.ai>
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Fixes #31093
What does this implement/fix? Explain your changes.
Using the reduced SVD leads to an incorrect covariance matrix when
n_samples < n_features.This PR uses the full SVD to compute the posterior covariance matrix when
n_samples < n_features, and keeps the reduced SVD whenn_samples > n_featuresfor performance in the largen_samplescase.The non-regression test
test_bayesian_covariance_matrixis added.