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...
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...and Random Forests....
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Sources:
The original AdaBoost paper by Robert E. Schapire and Yoav Freund
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And a follow up by co-created Schapire:
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The idea of using the weights to resample the original dataset comes from Boosting Foundations and Algorithms, by Robert E. Schapire and Yoav Freund
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Lastly, Chris McCormick's tutorial was super helpful:
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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
