scikit-learn: add which solver was used in sklearn Ridge regression : add clf.get_params(solver)

Describe the issue linked to the documentation

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html

solver{‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’, ‘lbfgs’}, default=’auto’ Solver to use in the computational routines:

‘auto’ chooses the solver automatically based on the type of data.

Suggest a potential alternative/fix

add how to find which solver actually was used

for example

>>> from sklearn.linear_model import Ridge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> clf = Ridge(alpha=1.0)
>>> clf.fit(X, y)
Ridge()
add
clf.get_params(solver)