Answer Posted / Mohit Kumar Prasad

The two main paradigms of ensemble methods in machine learning are: (1) Bagging, which creates multiple bootstrap samples from the original dataset and trains a separate model on each sample; (2) Boosting, which trains models iteratively by weighting instances based on their past errors and combining predictions.

Tell me what are the last machine learning papers you've read?

291

Do you have research experience in machine learning?

153

What are standardization and normalisation? Give one advantage of each over the other?

148

Tell us do you have research experience in machine learning?

224