AdaBoost Math with Example clearly explained Step by Step Machine Learning Ensembles

Adaptive Boosting (AdaBoost) Math Explained with example explained step by step. This is part 5 of Ensembles Technique.
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[JINGCUI-suse]

Very clear explanation, this is what I want!

[ositaonyejekwe]

image for undersamping and over sampling makes no sense. Can you explain what is the orange part and what is the blue part???so confusing...

[sibusisomtiyane]

Well explained 👏 👌, though I am not sure if the For loop and recursive formula are correctly defined. If the initial t = 0, doesn't that mean you will have f(0) = f(0 - 1) + lambda*g(0)... what is f(0-1)

[yurigansmith]

In this example the new weights for the formerly misclassified examples are increased, while the weights for the correctly classified are decreased (which seems reasonable to me at the moment). But if e_t becomes greater than 0.5, lambda_t becomes negative and the direction of the weight adaptation is swapped, which would lead to undersampling of the misclassified and oversampling of the correctly classified examples in the next round. Is lambda "allowed" to become negative in the first place? Somewhere (slides on boosting algorithms) I read that lambda is supposed to be non-negative, but I'm not sure if I understood the statement resp. context of the statement correctly.

[ishukothari]

I didnt understand that how the g function value is updating are we always assuming a new weak learner algorithm or the one which we select initially gets updated if yes then how ?

[meha]

You should mention the normalized method. I kill myself to find out how to normalize those numbers