Ensemble methods: bagging, boosting and stacking


This post was co-written with Baptiste Rocca. This old saying expresses pretty well the underlying idea that rules the very powerful "ensemble methods" in machine learning. Roughly, ensemble learning methods, that often trust the top rankings of many machine learning competitions (including Kaggle's competitions), are based on the hypothesis that combining multiple models together can often produce a much more powerful model. The purpose of this post is to introduce various notions of ensemble learning. We will give the reader some necessary keys to well understand and use related methods and be able to design adapted solutions when needed.

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