Ensemble Methods: Bagging, Boosting, and Stacking
Combine multiple models using ensemble techniques to improve prediction accuracy and robustness.
Ensemble methods combine predictions from multiple machine learning models to achieve better overall performance than any single model alone. Bagging (bootstrap aggregating) trains independent models on random subsets of data and averages their predictions, reducing variance—useful for high-variance, low-bias models like decision trees. Boosting sequentially trains models, with each new model focusing on errors made by previous ones, reducing bias—effective for weak learners. Stacking trains a meta-learner on predictions from base models, allowing it to learn when to trust which base model. Each approach exploits the wisdom-of-crowds principle: diverse, reasonably accurate models collectively outperform individuals. Ensemble methods are robust because…
Sign up free — one personalized lesson every day, matched to your role and goals.
Already have an account? Sign in