Ensemble learning involves a collection of machine learning methods aimed at improving the predictive performance of algorithms by combining many models. We will analyze the motivation behind using such methods to solve problems that arise from high bias and variance. Furthermore, we will present methods that allow the identification of bias and variance in machine learning models, as well as basic classes of ensemble learning methods.
Ensemble learning
Motivation
Ensemble learning aims to solve the problems of bias and variance. By combining many models, we can reduce the ensemble's error, while retaining the individual models' complexities. As we saw earlier, there is a certain lower limit imposed on each model...
