AdaBoost Explained: Simplifying the Power of Boosting Algorithms 💡

AdaBoost is one of those machine learning methods that seems so much more confusing than it really is. It's really just a simple twist on decision trees and random forests.

NOTE: This video assumes you already know about Decision Trees...
https://youtu.be/_L39rN6gz7Y
...and Random Forests....
https://youtu.be/J4Wdy0Wc_xQ

Sources:
The original AdaBoost paper by Robert E. Schapire and Yoav Freund
https://www.sciencedirect.com/science/article/pii/S002200009791504X

And a follow up by co-created Schapire:
http://rob.schapire.net/papers/explaining-adaboost.pdf

The idea of using the weights to resample the original dataset comes from Boosting Foundations and Algorithms, by Robert E. Schapire and Yoav Freund
https://mitpress.mit.edu/books/boosting

Lastly, Chris McCormick's tutorial was super helpful:
http://mccormickml.com/2013/12/13/adaboost-tutorial/

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0:00 Awesome song and introduction
0:56 The three main ideas behind AdaBoost
3:30 Review of the three main ideas
3:58 Building a stump with the GINI index
6:27 Determining the Amount of Say for a stump
10:45 Updating sample weights
14:47 Normalizing the sample weights
15:32 Using the normalized weights to make the second stump
19:06 Using stumps to make classifications
19:51 Review of the three main ideas behind AdaBoost

Correction:
10:18. The Amount of Say for Chest Pain = (1/2)*log((1-(3/8))/(3/8)) = 1/2*log(5/8/3/8) = 1/2*log(5/3) = 0.25, not 0.42.

#statquest #adaboost