What is stacking overfitting and how can it be prevented?

Stacking overfitting occurs when the meta-model learns noise from base model predictions.

Stacking can overfit if the meta-learner is trained on predictions generated from the same data used to train base models, causing data leakage. This leads to overly optimistic performance. Prevention techniques include k-fold cross-validation for generating out-of-fold predictions, using simple meta-models like logistic regression, and regularization.