The fit_transform docstrings of the VotingClassifier and VotingRegressor are inherited from the TransformerMixin:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

They don't really apply here and we should use the docstring from transform to explain what is returned:

Return c

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

Improve examples such that they are more incremental (in the import etc) without following strictly PEP8. It will make it nicer to read on the gallery generated online.

See:
gonum/gonum#845 (comment)

This should just be a matter of accepting a dict named line_search_kwargs or such and passing it through to the actual line search.

This was a feature request that came out of discussing #1034.

1. "Conclusion" section of "Getting started with Tablesaw" page contains broken link to "Java Docs".
   https://jtablesaw.github.io/tablesaw/gettingstarted#conclusion
2. "Exploring tables" section of "Getting started with Tablesaw" page contains broken link to "plotting".
   https://jtablesaw.github.io/tablesaw/gettingstarted.html#exploring-tables

I found several useful applications of pseudo-random number sampling in the past. In particular:

• Inverse transform sampling
• Gibbs sampling

(This issue serves a reminder to add the respective methods. Pull requests always welcome.)