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OK there is still one issue with np.byte_bounds being deprecated and used in joblib, this was already noted in joblib/joblib#1454, and can be tackled in a separate PR.

I wonder if the new tol criterion has an impact on the ongoing PR that aims to check good convergence tolerance #25948.

I took test_ridge.py from #25948 and enabled the tests with fit_intercept=False and ran the tests for sparse_cg, the intercept=False seems still fail:

pytest sklearn/linear_model/tests/test_ridge.py -k sparse_cg -ra

FAILED sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_unpenalized[wide-42-False-sparse_cg] - AssertionError: 
FAILED sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_unpenalized_hstacked_X[wide-42-False-sparse_cg] - AssertionError: 
FAILED sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_unpenalized_vstacked_X[wide-42-False-sparse_cg] - AssertionError: 

Let me know in case you had something else in mind!

Note the remaining errors all look like some formatting changes for numpy scalars in numpy development version, better to leave them for a separate PR. This is due to numpy/numpy#22449 that implements NEP 51.

For example:

E       AssertionError: Regex pattern did not match.
E        Regex: "The classes, \\['dog'\\], are not in class_weight"
E        Input: "The classes, [np.str_('dog')], are not in class_weight"

I think it is worth to acknowledge the changes in the Changed models section.
I agree with @thomasjpfan that we might not have a better way to do handle this case.

Merging my own PR with two approvals 😅

It looks like the remaining failures may be fixed by #27042