What is the difference between bagging and boosting ?
Ensemble learning is now an extremely effective method in machine learning, which allows models to improve precision and reliability by combining the results of several base models. The two methods of boosting and bagging are two well-known methods of ensemble learning that each have their method of improving the performance of models. In this thorough review, we'll explore the major differences between bagging and boosting as well as shed light on their fundamental concepts, benefits, and uses.
I. Bagging (Bootstrap Aggregating):
Bagging is a sequential ensemble learning method that seeks to reduce variation and improve the stability of models. The word "bootstrap" in bagging refers to the statistical method of sampling using replacement. The essential steps to bagging are:
Bootstrap Sampling Bagging begins by creating multiple bootstrap samples using the dataset. Each sample of Bootstrap is created by randomly securing instances from the dataset and replacing them. This produces several subsets, each of which has variations due to the sampling procedure.
