7.1 Intro to ensemble methods (L07: Ensemble Methods)

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In lecture 7, we are discussing ensemble methods, including majority voting, bagging, random forests, stacking, and gradient boosting -- those are some of the most popular and widely used applied ML methods of all time! :)

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This video is part of my Introduction of Machine Learning course.

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  1. Sebastian Raschka
  2. random forest
  3. ensemble methods
  4. machine learning
  5. boosting
  6. gradient boosting
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Wow, I stumbled across your lectures & specifically had to look for it again, since I think you do a fantastic job of explaining things and now I find out that you are behind mlxtend. I LOVE the sequential feature selector you created. It literally is a game changer in some of my projects. Big fan here!

climbscience
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The insight into what models are currently popular in the industry is really helpful! I've been using logistic regression for almost everything because I like seeing the probabilities but it sounds like I should be looking into Gradient Boosting and XGBoosting.
I've been reading your Python Machine Learning book in preparation for graduate school this fall and it's helped me a lot. Thank you!

nak