Here is a question that sounds like a trick: can you build an accurate classifier out of models that are barely better than flipping a coin?
Surprisingly, yes. That is the whole idea behind boosting, and AdaBoost is the algorithm that made it famous. I built it from scratch and dropped it into an interactive demo — here's how it actually works, real math, no hand-waving.
Play with the live version: https://dev48v.infy.uk/ml/day21-adaboost.html
The weak learner: a decision stump
AdaBoost's building block is the simplest classifier you can imagine: a decision stump. It is a decision tree with exactly one split. Look at one feature, compare it to one threshold, and call everything on one side "+1" and everything on the other side "−1". That's it. One line, one cut.
