What is boosting bias reduction intuition?

Boosting reduces bias by sequentially correcting errors made by previous models.

Boosting builds models iteratively where each new model focuses on the residual errors of the previous ensemble. This allows the ensemble to gradually improve approximation of complex functions. Unlike bagging, boosting does not rely on averaging independent models but instead constructs a strong learner from weak learners, reducing bias significantly.