Bagging and Boosting in Machine Learning

Bagging is a technique used in ML to make “weak classifiers” strong enough to make good predictions. The technique which we use is - we make use of many weak classifiers to make a prediction on our *data and then combine the results by taking their average or by picking the most likely prediction made by the majority of these weak models. The main catch here is that all the weak classifiers used are independent i.e…