What is the umbrella term for methods that train multiple models and combine their outputs to improve predictive performance?

Get ready for the GARP Risk and AI Exam with flashcards and multiple choice questions. Each question comes with hints and explanations. Prepare for success!

Ensemble techniques are approaches that train multiple models and combine their outputs to improve predictive performance. The core idea is that different models capture different patterns and errors, so blending their predictions often yields greater accuracy than any single model. Bagging trains several models in parallel on different bootstrap samples and averages or votes their outputs, which helps reduce variance. Boosting builds models sequentially, with each new model focusing on correcting the mistakes of the previous ones, typically reducing bias and improving accuracy. Stacking combines the predictions of several base models using a meta-model to produce the final prediction. Since this umbrella term covers these varied strategies, it best fits the description. The other terms refer to specific methods within this broader category.