What is Boosting?

Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning. Boosting combines multiple weak learners into a strong learner. Each weak learner is typically a simple model, such as a decision tree. The algorithm iteratively trains new weak learners, with each one focusing on the mistakes made by the previous learners. The final prediction is a weighted combination of the predictions from all weak learners.