Practical Exercises with Tree-Based Models

In this final section, we will engage in practical exercises that involve building, tuning, and evaluating tree-based and ensemble models on real-world datasets. These exercises are designed to reinforce the concepts learned throughout the chapter and demonstrate how to effectively apply these models in complex machine learning scenarios. By the end of this section, we will have hands-on experience that we can leverage in our own ML projects.

Exercise 1: Building and Evaluating a Decision Tree Classifier

In this exercise, we'll build and evaluate a basic decision tree classifier.

Implementation steps:

1. Load libraries.
2. Load the dataset.
3. Split the data.
4. Create and train the classifier.
5. Make predictions.
6. Evaluate performance.

Exercise 2: Hyperparameter Tuning with Random Forests

We'll fine-tune a random forest classifier using grid search to find the optimal parameters.

Implementation steps:

1. Load libraries.
2. Load the dataset.
3. Split...