10-601 Ensembles 1
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Jump to navigationJump to searchThis a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- Ensemble Methods in Machine Learning, Tom Dietterich
- A Short Introduction to Boosting, Yoav Freund and Robert Schapire.
Summary
You should know how to implement these ensemble methods, and what their relative advantages and disadvantages are:
- (Ziv - not sure if can do in one lecture if we do boosting) Bagging
- Boosting
- Stacking
- Multilevel Stacking
- (Ziv - not sure if I will do this) The "bucket of models" classifier
- Random forest
