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This PR adds native support for missing values (NaNs) in UpliftTree and
UpliftRandomForest.

Each candidate split evaluates both possible NaN routing directions
(left/right) and learns the optimal routing per node, similar to
scikit-learn’s decision tree behavior.

The learned NaN routing is stored in each tree node and applied
consistently during training, pruning, filling, and prediction.

This resolves #802.

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CLAassistant commented Dec 29, 2025

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@aman-coder03
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The Read the Docs build failure appears to be due to Cython extensions requiring a compiler in the RTD environment.
No documentation-related files were modified in this PR.
Happy to adjust the RTD config if you’d like me to handle this

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Support for Inputs with NaN in Uplift Trees/Uplift RandomForest

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