treelite.sklearn.export_model should preserve sklearn-compatible child estimator class metadata for random forest classifiers

[coderabbitai]

Summary

treelite.sklearn.export_model should export scikit-learn-compatible classifier metadata for child estimators in exported random forest classifiers.

Today, cuML needs a local workaround when converting a RandomForestClassifier to scikit-learn: after calling the CPU export path, it manually patches each exported child estimator so that:

• the forest-level classes_ retains the original class labels
• each child estimator's classes_ uses sklearn-style encoded class indices ([0, 1, ..., n_classes - 1])
• each child estimator's n_classes_ matches the parent classifier

This behavior is currently implemented downstream in RAPIDS cuML, but it appears to be a responsibility that belongs in Treelite's sklearn export layer.

Rationale

scikit-learn stores classifier metadata differently at the forest level vs. the individual child trees. If treelite.sklearn.export_model exports the same classes_ metadata everywhere, downstream projects need ad hoc fixups to restore sklearn-compatible child estimator state.

Moving this logic upstream would:

• keep sklearn-export behavior consistent at the source
• remove downstream patching/hacks in RAPIDS cuML
• improve compatibility for any Treelite consumer exporting sklearn random forest classifiers

Affected area

• treelite.sklearn.export_model
• sklearn random forest classifier export path
• exported child estimator metadata (classes_, n_classes_)

Observed downstream workaround

RAPIDS cuML PR #8291 adds a post-processing step in python/cuml/cuml/ensemble/randomforestclassifier.py to patch exported child estimators after CPU export.

PR: NVIDIA/cuml#8291
Review comment: NVIDIA/cuml#8291 (comment)

Requested by: @csadorf

Expected behavior / acceptance criteria

• Exported sklearn random forest classifiers preserve the original labels at the forest level (forest.classes_)
• Exported child estimators use sklearn-compatible encoded labels for estimator.classes_
• Exported child estimators expose the correct estimator.n_classes_
• Behavior matches a natively fitted scikit-learn RandomForestClassifier, including cases with non-contiguous class labels
• Downstream projects such as cuML can remove local metadata patching once they adopt the updated Treelite behavior