What is boosting in machine learning?

Boosting is a sequential ensemble method that focuses on correcting previous model errors.

Boosting trains weak learners sequentially, where each new model emphasizes samples that were misclassified by previous models. The final prediction is a weighted combination of all weak learners. This iterative correction reduces bias and improves predictive accuracy.