Tree structures offer a hierarchical method for organizing and accessing data, finding significant application in machine learning for tasks ranging from efficient searching to building interpretable models. This chapter examines how tree structures function and where they fit into the ML toolkit.

We will cover:

• Fundamentals: Review basic tree terminology and concepts like nodes, edges, height, and depth.
• Binary Search Trees (BSTs): Their properties and operations (search, insert, delete) for efficient data retrieval, typically achieving $O(\log n)$ average time complexity for searches.
• Balanced Trees: Understand the necessity of balancing (e.g., AVL, Red-Black trees) to prevent worst-case $O(n)$ performance degradation in BSTs with skewed data.
• Decision Trees & Ensembles: Connect the abstract tree structure to concrete ML models like Decision Trees, Random Forests, and Gradient Boosted Trees, understanding how they partition feature space.
• Tree Traversal: Learn methods like in-order, pre-order, and post-order traversal, useful for model inspection or feature extraction.

By the end of this chapter, you will understand how to select and utilize tree-based structures for specific machine learning problems and recognize their performance characteristics.