Basic understanding of the Adaboost algorithm

Question

I am studying a beginner trying to understand how Adaboost works.

I read many articles explaining how Adaboost uses a set of weak * classifiers * to create a strong classifier.

However, I seem to have a problem understanding the statement that " Adaboost creates a strong classifier ."

When I looked at the Adaboost implementations , I realized that it doesn’t "actually" create a strong classifier, but somehow in the "TEST PHASE" section on " how to use a set of weak classifiers to get more accurate results, " which, in turn, It acts as a strong classifier " Collectively strong>".

Thus, technically there is NO SINGLE STRONG CLASSIFIER (but a set of weak classifiers collectively acts as a strong classifier).

Please correct me if I am wrong. It would be nice if someone could make some comments on this .

Answer 1

A classifier is a black box that receives input (object vectors) and returns output (marked vectors). Therefore, to call something a classifier, you do not care what it is , what , and not how it does it. The AdaBoost classifier can be considered as a black box, so it really is one classifier, even if it uses several weak classifiers inside to create such an output.