Differences between bagging, boosting and stacking in Machine Learning

Bagging, Boosting, and Stacking are popular ensemble methods in machine learning. Bagging reduces variance by averaging predictions from multiple models, making it ideal for high-variance algorithms like decision trees. Boosting builds sequential models to reduce bias, focusing on correcting previous errors. Stacking combines multiple models to generate intermediate predictions, which are used by a final model for improved accuracy. Each technique serves a different purpose in enhancing model performance.

For more details, check out the full article: Bagging vs Boosting in Machine Learning | Stacking in Machine Learning.