Machine Learning Fundamentals

3

Supervised Learning: Key Steps

Learning Objectives

By the end of this chapter, you will be able to:

• Explain the difference between training, validation, and testing sets
• Perform data partitioning for split or cross validation
• Describe the different metrics to evaluate performance
• Choose the performance metric that fits the purpose of the study
• Perform error analysis

This chapter explains the methodology to approach a machine learning classification problem.

Introduction

In the previous chapter, we saw how to solve data problems using unsupervised learning algorithms and applied the concepts that we learned to some real-life datasets. We also learned how to compare the performance of various algorithms and studied two different metrics ...