Updated feature selection tutorial

[gtduncan]

Describe your changes

Edits https://github.com/ploomber/sklearn-evaluation/pull/294 to pass lint check and allows appearance in navbar

Original tutorial: @bbeat2782

Checklist before requesting a review

• [x] I have performed a self-review of my code
• [x] I have added thorough tests (when necessary).
• [x] I have added the right documentation in the docstring and changelog (when needed)

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:books: Documentation preview :books:: https://sklearn-evaluation--304.org.readthedocs.build/en/304/

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[idomic]

@gtduncan looks like you have a conflict on changelog.md. Please mark as ready for review when you're ready. Also I can't see the guide via the link, please fix

[gtduncan]

Docs building was failing and I found it was one of the cells timing out— specifically, the cell describing forward selection in the 2.1 Forward Selection portion. I ran just that cell, found it was taking 90 seconds to run, and changed the execution time in conf.py to mitigate this– let me know if there's something else you'd want to do about it because it's a pretty large jump. I'm also confused how the docs passed on @bbeat2782's CI as well. I also added @neelasha23's requested edits from the original PR

[neelasha23]

docs build is failing @gtduncan

[edublancas]

I think the problem is here:

nbclient.exceptions.CellTimeoutError: A cell timed out while it was being executed, after 120 seconds.
The message was: Cell execution timed out.
Here is a preview of the cell contents:
-------------------
['from sklearn.feature_selection import SequentialFeatureSelector', 'from sklearn.ensemble import RandomForestClassifier', 'from sklearn_evaluation import plot', 'rfc = RandomForestClassifier()', 'forward_select = SequentialFeatureSelector(']
...
[')', 'forward_select.fit(X_clf_train, y_clf_train)', 'features = forward_select.get_feature_names_out()', 'rfc.fit(X_clf_train[features], y_clf_train)', 'plot.feature_importances(rfc)']
-------------------

let's try reducing the data size or any other parameter that affects runtime

worst case, we can increase the cell timeout but that should be our last resort since it'll slow down the doc building process

[gtduncan]

I was able to get the cell you mentioned to run quicker by changing the n_features_to_select parameter from 'auto' to 0.1 seen here: forward_select = SequentialFeatureSelector(rfc, direction='forward', n_features_to_select=0.1)— however, in the following cell where backward_select = SequentialFeatureSelector(rfc, direction='backward', n_features_to_select='auto') is called, regardless if I change the n_features_to_select parameter, the cell takes around 2 and a half minutes to execute. Any ideas on how to reduce this runtime?

[edublancas]

reducing the number of rows will help with runtime (I'm guessing that's the rfc parameter), how large it is?

[gtduncan]

The rfc parameter is RandomForestClassifier(). I've tried lowering the n_estimators parameter in that model as well, which seems to make the forward selection run pretty quickly, but the backward selection still times out. It does run locally, but I think it may just be too slow for the CI... I'll keep looking into solutions

[edublancas]

I think let's make it a non-runnable cell (create it as a markdown cell in Jupyter). just copy whatever output it produces