What is the primary purpose of using boosting in ensemble methods?

Boosting is a powerful ensemble learning technique that primarily focuses on improving model performance by sequentially correcting mispredictions made by previous models in the ensemble. The essence of boosting lies in its iterative approach, where models are trained one after another, each new model focusing on the instances that were misclassified by the earlier models.

In each iteration, the algorithm adjusts the data weights, placing more emphasis on those instances that were incorrectly predicted. This process enables the ensemble to gradually improve its accuracy by learning from the mistakes of prior models, effectively reducing bias and enhancing overall predictive performance.

This method contrasts with other ensemble techniques, such as bagging, which mainly aim to reduce variance through multiple independent models working on different subsets of data. By understanding that boosting builds models in a sequential manner to specifically target previous inaccuracies, it's clear why the primary purpose of boosting in ensemble methods is to sequentially correct those mispredictions.