This chapter introduces the essential practice of evaluating machine learning models. Building a model is only part of the process; determining its effectiveness is equally important before it can be used reliably. We will examine why assessing model performance is a fundamental step in the machine learning workflow.

You will learn about evaluation metrics, the quantitative tools used to measure how effectively a model makes predictions or estimates. We will differentiate between the two primary types of supervised learning problems addressed in this course: classification, which involves assigning data points to predefined categories, and regression, which involves predicting continuous numerical values. This chapter lays the groundwork by defining these concepts and providing an overview of the standard evaluation procedure.

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

• Define what a machine learning model is in the context of evaluation.
• Explain the reasons for evaluating model performance.
• Understand the purpose of using evaluation metrics.
• Distinguish between classification and regression tasks with examples.
• Outline the high-level steps involved in the model evaluation process.