Traverse Trees for ML with DFS & BFS

Overview

Data that requires decisions and classifications are everywhere. Decision trees help to create solid data inferences for some of the most common types of machine learning problems. To take advantage of this structure, you need to understand how to properly traverse and build rulesets from decision trees. In this course, you'll learn the fundamentals of decision trees, understanding how to implement the structures in Java. From here, you'll explore some different methods of tree traversals, focusing on BFS and DFS. With BFS and DFS, you'll be able to apply tree traversals to generate tree rulesets. With this knowledge, you'll be equiped to implement and traversal decision trees. This course is for Java developers with a solid programming background, focusing on decision trees, BFS, DFS, and rule generation for machine learning and data classification. A solid understanding of Java programming is crucial for implementing decision trees and traversal algorithms. Additionally, some familiarity with trees as a data structure will help, as decision trees rely on hierarchical structures. By the end of this course, you'll have the skills to confidently implement tree traversal algorithms like BFS and DFS, and generate powerful rules from decision trees to tackle real-world machine learning problems.

Syllabus

Fundamentals of Tree Search Algorithms

Tree searching algorithms are a core method for traversing tree-based data structures. In this module, we'll explore the strucutre of decision trees and understand how a breadth-first and depth-first search for be applied to traverse decision tree structures.

Implementing and Analyzing Tree Traversals

With an understanding of the theory of tree traversals, we can now move towards an implementation of our traversal algorithms. In this module, we'll explore how DFS and BFS can be implemented Java. We'll also take a look at how these algorithms can be analyzed to understand both time complexity and potential use cases.

Generating Tree Rules with BFS and DFS

One of the main applications of BFS and DFS for decision trees is the creation of tree rules. In this module, we'll see how both BFS and DFS can be applied to generate tree rules for a decision tree. We'll also explore how these approaches compare to other common tree rule generations such as ID3 and CART.