Practical Exercises with Advanced Logistic Regression

In this final section, we will engage in practical exercises that involve building, tuning, and evaluating logistic regression models for various datasets. These exercises are designed to reinforce the concepts learned throughout the chapter and demonstrate how to effectively apply logistic regression and its extensions in real-world scenarios. By the end of this section, we will have hands-on experience that can be leveraged in our own machine learning projects.

Exercise 1: Building a Regularized Logistic Regression Model

In this exercise, we will build a logistic regression model using the Breast Cancer dataset, applying both Lasso and Ridge regularization techniques.

Implementation steps:

1. Load libraries.
2. Load the dataset.
3. Split the data.
4. Create and train a Lasso regularized Logistic Regression model.
5. Make predictions with the Lasso model.
6. Evaluate the...