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Use fold i's SL fit for fold i validation predictions in cv_sl (#437) - #438
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…se#437) Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014KeUvbnmnFCYzN3pvfQyEA
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As @ctesta01 already pointed out in #437, I also came across this potential bug in
cv_sl():In
cv_sl(), the validation-set SuperLearner predictions for foldiare computed withfold_fits[[1]]rather thanfold_fits[[i]].Fold 1's SL was trained on the validation rows of folds 2..V, so the "SuperLearner" row of
cv_riskis scored in-sample on (V-1)/V of the observations, while the candidate learners' columns remain honestly cross-validated. As a result, theSL's CV risk is optimistically biased relative to its candidates.
I suggest a one-line change (
[[1]]->[[i]]), matching every other line in that loop.