Practical Exercises in Cross-Validation and Evaluation

In this final section, we’ll engage in practical exercises that involve building, tuning, and evaluating models using cross-validation and performance metrics. These exercises are designed to reinforce the concepts explored throughout the chapter, including model selection, hyperparameter tuning, and generalization evaluation. By completing these exercises, we’ll solidify our understanding of how to effectively assess model performance and select the best model for real-world applications.

Exercise 1: Cross-Validating a Logistic Regression Model

We’ll evaluate a logistic regression classifier using k-fold cross-validation and report multiple metrics.

Implementation steps:

1. Load libraries.
2. Load the dataset.
3. Cross-validate and collect metrics.

Exercise 2: Tuning Hyperparameters with Grid Search

We’ll perform hyperparameter tuning using GridSearchCV and compare the results.

Implementation steps:

1. Load...