Thanks for using LightGBM and taking the time to report this!

We'd welcome this addition, would you like to contribute it?

And a side question.... do you think it's an oversight that scikit-learn's estimator checks don't enforce this? We follow https://scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.check_estimator.html in LightGBM's tests to try to catch such things

Using scikit-learn==1.3.2 (the latest released version as of this writing), check_estimator() says LGBMClassifier and LGBMRegressor are compliant with scikit-learn's expectations for estimators.

import lightgbm as lgb
from sklearn.utils.estimator_checks import check_estimator

check_estimator(lgb.LGBMClassifier())
check_estimator(lgb.LGBMRegressor())

But in the SLEP you linked, it says the following:

Backward Compatibility
All estimators should implement the feature_names_in_ and get_feature_names_out() API. This is checked in check_estimator...

I would very much like to contribute to LightGBM and this seems like a great issue, with @ravwojdyla's blessing, I'd be happy to make this contribution.

@nicklamiller sounds great - thank you!

Do either of you know the answer to my question about check_estimator() from the latest scikit-learn not complaining about this?

Backward Compatibility
All estimators should implement the feature_names_in_ and get_feature_names_out() API. This is checked in check_estimator...

@jameslamb I agree that based on SLEP007, this functionality should be implemented in check_estimator and does not appear to be. Here's a somewhat recent issue of sklearn estimators that lack(ed) this attribute, it looks like the attribute it is only checked/created if missing when _validate_data is called.

I can open an issue in sklearn and propose this behavior is more rigorously checked with check_estimator.

Thanks very much for the link to scikit-learn/scikit-learn#27907 @nicklamiller !

Please link to this issue from whatever one you create in scikit-learn.