Stacking Ensemble Model

A stacking ensemble model combines predictions from several different base learners by training a meta-learner on their out-of-fold predictions to produce a final prediction.

스태킹 앙상블 모형은 여러 다른 기본 학습기의 예측값을 결합하되, 이들의 교차검증 외부폴드 예측값으로 메타학습기를 학습시켜 최종 예측을 만든다.

How do I combine several different machine learning models to improve prediction?

여러 머신러닝 모형을 결합하여 예측 성능을 어떻게 높이나요?

A stacking ensemble trains several diverse base models and then fits a meta-learner, often a regularized linear model, on their cross-validated predictions to produce a combined prediction. It fits when different model types capture different patterns and a small gain in accuracy justifies extra complexity.

When to use Stacking Ensemble Model

• Several diverse models, such as linear, tree-based and neural, with comparable accuracy
• A labelled outcome and enough data for nested cross-validation
• Prediction accuracy matters more than interpretability

When to use something else

• Interpretability or simplicity is required; use a single regularized regression or tree model
• Small datasets where the meta-learner would overfit; use simple averaging or a single tuned model

Assumptions and requirements

• Out-of-fold predictions used to train the meta-learner to avoid leakage
• Diverse base learners
• Sufficient labelled data

What the analysis reports

• Cross-validated performance of each base model
• Cross-validated performance of the stacked model
• Meta-learner weights
• Comparison table of models

How to report it

The stacking ensemble achieved a cross-validated RMSE of [value] (SD [value]), compared with [value] for the best single base model.

Related methods

• Random Forest
• Gradient Boosting Machine
• Bagging

Run Stacking Ensemble Model on Quorant