Having learned to implement supervised learning models and preprocess data, the next logical step is determining how well these models perform and how to select the best configuration. Simply evaluating a model on the data it was trained on can be misleading. A model might perform perfectly on training data but fail significantly on new, unseen data. This chapter addresses this challenge.

We will examine the common problems of overfitting, where a model learns the training data too well, including its noise, and underfitting, where a model is too simple to capture the underlying patterns. You will learn techniques to get a more realistic assessment of model performance.

Specifically, this chapter covers:

• The concepts of overfitting and underfitting.
• Splitting datasets into training and testing sets using Scikit-learn's train_test_split function for initial evaluation.
• Applying cross-validation, particularly K-Fold and Stratified K-Fold, for a more reliable estimate of how a model will perform on unseen data.
• Using Grid Search (GridSearchCV) to systematically tune model hyperparameters and find optimal settings.

By the end of this chapter, you will be equipped to rigorously evaluate your machine learning models and make informed decisions about model selection and configuration.