It appears that the docs for Logistic Regression differ based on solvers and penalties. The "penalty" parameter states that "The ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers support only l2 penalties," while the "solver" parameter states that "‘newton-cg’, ‘lbfgs’, ‘sag’ and ‘saga’ handle L2 or no penalty" (attaching some screenshots). This was actually a little unclear to me, as I wasn't sure if the n

Collection of follow-ups to #5827. These can/should be broken out into individual PRs. Many are relatively straightforward and would make a good first PR.

General

• Documentation (none was added in original PR).
• Release notes.
• Example notebook.
• Double-check how sm.tsa.arima.ARIMA works with fix_params (it should fail except when the fit method is statespace

In some figure, the axis name is cropped. We should probably used tight_layout explicitly:

https://imbalanced-learn.org/stable/auto_examples/over-sampling/plot_shrinkage_effect.html#sphx-glr-auto-examples-over-sampling-plot-shrinkage-effect-py

See:
gonum/gonum#845 (comment)

tfd.Categorical.quantile not working

Since the default output is meant to be human-readable, would it make sense to add thousands separators to make the output more easily readable?