Fundamentals of Data Science

10: Ensemble learning

Abstract

This chapter covers ensemble learning, a powerful machine learning technique that makes use of the variety of several models to boost the reliability and accuracy of predictions. In supervised, unsupervised, and semi supervised learning scenarios, ensemble learning refers to a variety of methods for merging the results of distinct models. The chapter starts out by introducing the basic ideas behind ensemble learning and highlighting how crucial it is for improving model performance. It explores the incentives for ensemble learning and demonstrates how merging various models can minimize flaws and produce better outcomes. The chapter explores the parameters for selecting base classifiers in the context of supervised ...