Lecture
Ensemble Methods: Random Forest
Description
This lecture covers ensemble methods, focusing on random forests as a powerful technique to combine multiple decision trees for classification. It explains the concepts of bagging, stacking, and boosting, along with the sampling strategies and algorithm used in random forests. The instructor discusses why ensemble methods work by leveraging the diversity of base classifiers and explores the characteristics, strengths, and weaknesses of random forests.
Official source
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