How does Ensemble Methods (Bagging & Boosting) work

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Answer

Bagging trains many models independently on different random samples of data and averages their results, while boosting trains models sequentially, each one focusing on correcting the previous model's mistakes.

Real-world example A random forest, which uses bagging, and a gradient boosting model are both common ensemble methods used to improve prediction accuracy.

Common follow-ups: What is Predictive Modeling, How is Ensemble Methods (Bagging & Boosting) evaluated in practice, What tools are commonly used for Ensemble Methods (Bagging & Boosting)

Predictive Modeling topics: Introduction to Predictive Modeling Time Series Forecasting Regression-Based Prediction

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