The Two Essential Algorithms for Making Predictions

• Published in: Machine Learning with Spark and Python pp. 1 - 2
• Main Author: Bowles, Michael
• Format: Book Chapter
• Language: English
• Published: United States John Wiley & Sons 2020; John Wiley & Sons, Incorporated; John Wiley & Sons, Inc
• Edition: 2nd Edition
• Subjects: algorithm performance; ensemble methods; General Engineering & Project Administration; General References; machine learning; penalized regression methods; predictive model
• ISBN: 1119561930; 9781119561934
• DOI: 10.1002/9781119562023.ch1

This chapter covers two classes of algorithms for solving function approximation problems: penalized linear regression methods and ensemble methods. It introduces machine learning students to both of these algorithms, outlines some of their characteristics, and reviews the results of comparative studies of algorithm performance in order to demonstrate their consistent high performance. One of the problems spurring the development of ensemble methods has been the observation that some particular machine learning algorithms exhibit instability. The basic idea with ensemble methods is to build a horde of different predictive models and then combine their outputs—by averaging the outputs or taking the majority answer. The individual models are called base learners. The chapter discusses the process of building predictive models and also presents an overview of the key concepts discussed in this book.