<ul><li>Bagging reduces unpredictability in a model's predictions and stabilizes predictions by creating multiple smaller models that vote on the final answer.</li><li>Each model in Bagging leaves out some samples, known as out-of-bag samples, to calculate an unbiased estimate of model accuracy.</li><li>Boosting builds models one at a time, focusing on fixing errors of the previous model, and aims to reduce bias and achieve high accuracy on complex data.</li><li>Random Forest leverages Bagging by training multiple decision trees on different subsets of data and features, while Gradient Boosting improves accuracy by focusing on previous errors.</li></ul>

A Beginner’s Guide to Bagging and Boosting in Machine Learning with examples

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