What is boosting in machine learning?

What is boosting in machine learning?

Boosting is an ensemble technique that combines multiple weak learners to create a strong learner, improving accuracy.

How does boosting differ from bagging?

While boosting focuses on correcting errors of weak learners sequentially, bagging builds models independently to reduce variance.

Can you give me an example of a boosting algorithm?

Sure! AdaBoost and Gradient Boosting are popular boosting algorithms used in various machine learning tasks.

What are the advantages of using boosting?

Boosting can significantly improve model performance, reduce bias, and is effective for both classification and regression tasks.

Are there any disadvantages to boosting?

Yes, boosting can be sensitive to noisy data and outliers, and it may lead to overfitting if not properly tuned.