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Honor max_iter=0 in ordinary and staged predictions - #457
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rudycelekli wants to merge 2 commits into
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rudycelekli wants to merge 2 commits into
rudycelekli wants to merge 2 commits into
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The prediction iteration guards treat zero as an omitted limit and use every fitted tree. Honor a zero boosting-iteration count: ordinary predictions return the fitted initial distribution and staged predictions return an empty list. Positive limits and None retain their behavior.
Eight regressor/classifier cases cover zero, one and all iterations, distribution and point predictions, probability predictions, and empty staged results. Expected parameters are reconstructed from the initial distribution and the requested fitted stages.
Validation on Python 3.11.15, scikit-learn 1.6.1, NumPy 2.4.6 and SciPy 1.17.1: this independent branch's regression tests and full configured pre-commit hooks pass. The unchanged upstream slow suite passes 169 tests, with 2 xfailed and 22 xpassed. A private composition of the six proposed fixes passes 201 tests with the same expected/unexpected-pass statuses; final organized regression tests pass all 32 cases and combined lint passes. The production files are identical across that integration run and the final branches; test organization was verified separately.
Implementation and tests are AI-assisted, with additional independent automated correctness/duplicate review. Numerical cases use synthetic fixtures and analytic/SciPy oracles. Hosted CI and maintainer acceptance are separate from these local results.