10. Tree-Based Methods#
Tree-based methods are a family of powerful and interpretable machine learning techniques. They use decision trees to model complex decision-making processes as a series of simpler, sequential choices. Each choice is based on a feature of the data and leads to a prediction or another choice. This way, tree-based methods can capture intricate relationships in the data while maintaining a human-readable structure.
Tree-based methods are popular and versatile in the machine learning community. They can explain how they make predictions, which is important in domains like medicine or finance. They can also handle different types of data, both for classification and regression tasks. Moreover, they can deal with interactions and non-linearities in the data, without requiring much feature engineering.
Tree-based methods can also achieve high performance, thanks to advanced techniques like Random Forests and gradient boosting machines. These are ensemble methods that combine multiple decision trees to create more robust and accurate models.
In this chapter, we will explore the details of decision trees, how to build them, how to avoid overfitting, and how to use ensemble methods. By the end of this chapter, you will have a comprehensive understanding of how tree-based methods work and how to apply them to various problems.