Kayak-share demand already has two decent base trainers. A third model is trained on those models' scores rather than on a simple average. Which technique is that?
Select an answer to reveal the explanation.
Short Explanation
Two decent base trainers already exist. Train a third model on those scores and that is stacking, not a flat average. Personalize is a recommender, and dropout is a regularizer, not a meta-learner.
Full Explanation
Stacking trains a second-level learner on the outputs of base models, which is not the same as a flat ensemble average. Personalize is a managed recommender. Dropout is a neural-net regularizer, not a meta-learner.