MACHINE LEARNING

Detailed explanation-1: -Bagging is a parallel ensemble learning method, whereas Boosting is a sequential ensemble learning method. Both techniques use random sampling to generate multiple training datasets. Both the techniques rely on averaging the N learner’s results or Majority voting to make the final prediction.

Detailed explanation-2: -In bagging, weak learners are trained in parallel, but in boosting, they learn sequentially. This means that a series of models are constructed and with each new model iteration, the weights of the misclassified data in the previous model are increased.

Detailed explanation-3: -Ensemble learning combines multiple machine learning models into a single model. The aim is to increase the performance of the model. Bagging aims to decrease variance, boosting aims to decrease bias, and stacking aims to improve prediction accuracy. Bagging and boosting combine homogenous weak learners.