A weak learner is an algorithm that performs relatively poorly—generally, the accuracy obtained with the weak learners is just above chance. It is often, if not always, observed that weak learners are computationally simple. Decision stumps or 1R algorithms are some examples of weak learners. Boosting converts weak learners into strong learners. This essentially means that boosting is not an algorithm that does the predictions, but it works with an underlying weak ML algorithm to get better performance.
A boosting model is a sequence of models learned on subsets of data similar to that of the bagging ensembling technique. The difference is in the creation of the subsets of data. Unlike bagging, all the subsets of data used for model training are not created prior to the start of the training. Rather, boosting builds a first model with an ML algorithm that...
