As @desertnaut mentioned in the comment
No weak learner becomes strong; it is the ensemble of the weak learners that turns out to be strong
Boosting is an ensemble method that integrates multiple models(called as weak learners) to produce a supermodel (Strong learner).
Basically boosting is to train weak learners sequentially, each trying to correct its ...
In my opinion, it is not because ensemble methods are not good, just the state-of-the-art and Kaggle competitions are two different fields.
Kaggle competitions can be understood as an industry project where the target (accuracy, distance value, etc) is the most important, and they can select some computationally expensive way such as ensemble methods to ...
In Boosting, we improve the overall metrics of the model by sequentially building weak models and then building upon the weak metrics of previous models.
We start out by applying basic non-specific algorithms to the problem, which returns some weak prediction functions by taking arbitrary solutions (like sparse weights or assigning equal weights/attention). ...
You take a bunch of weak learners, each of them trained on a subset of the data.
You then just get all of them to make a prediction, and you learn how much you can trust each one, resulting in a weighted vote or other type of combination of the individual predictions.