A practitioner builds a stacking ensemble with logistic regression, gradient boosting, and random forest as base learners, and a ridge regression as the meta-learner. What is the critical data hygiene requirement for training the meta-learner?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because if base learners' in-sample predictions are used as meta-learner features, overfitted base models produce overly optimistic predictions on their own training data, leaking information to the meta-learner. Cross-validation ensures the meta-learner trains on out-of-fold predictions where each observation was predicted by a base learner that never saw it.
Full explanation below image
Full Explanation
A is correct because if base learners' in-sample predictions are used as meta-learner features, overfitted base models produce overly optimistic predictions on their own training data, leaking information to the meta-learner. Cross-validation ensures the meta-learner trains on out-of-fold predictions where each observation was predicted by a base learner that never saw it. B is wrong; base learners can have different hyperparameters (diversity is beneficial). C is wrong; a test set is for final evaluation, not meta-learner training. D is wrong; this split wastes data and doesn't address the information leakage.