Model Selection

Chapter 1: What is a Model?

In this introduction, we’ll discuss what models are along with a few examples of real-life models.

Chapter 2: Common guiding principles

In this chapter, we’ll discuss some common guiding principles when building models, regardless of the task.

Chapter 3: Evaluation techniques

In this chapter, we’ll discuss evaluation techniques such as k-fold cross-validation, holdout, etc.

Chapter 4: Evaluating regression

In this chapter, we’ll discuss common metrics used to evaluate regression models.

Chapter 5: Evaluating classification

In this chapter, we’ll discuss common metrics used to evaluate classification models.

Chapter 6: Evaluating clustering

In this chapter, we’ll discuss common metrics used to evaluate clustering models.

Chapter 7: Hyperparameter tuning

In this chapter you’ll learn about the concept of hyperparameter tuning, including grid search and random search.