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microsoft / LightGBM

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
https://lightgbm.readthedocs.io/en/latest/
MIT License
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Compute partial dependencies from learned trees #4578

Closed ChristophAymannsQC closed 3 years ago

ChristophAymannsQC commented 3 years ago

Summary

Partial dependencies can be computed directly from the data distribution in the leaves of the trees in the booster, rather than by calling predict, see references below. This tends to be faster. It would be nice if LightGBM allowed for direct computation of the partial dependencies using this method.

Motivation

Partial dependency plots are widely used to interpret a model. Providing a fast method for computing them would be a big asset.

References

StrikerRUS commented 3 years ago

Closed in favor of being in #2302. We decided to keep all feature requests in one place.

Welcome to contribute this feature! Please re-open this issue (or post a comment if you are not a topic starter) if you are actively working on implementing this feature.

github-actions[bot] commented 1 year ago

This issue has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/microsoft/LightGBM/issues including a reference to this.

jameslamb commented 1 year ago

Sorry, this was locked accidentally. Just unlocked it. We'd still love help with this feature!