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.
Traverse Trees for ML with DFS & BFS
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Advanced level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Analyze the differences between Breadth-First Search and Depth-First Search to understand when to use each approach.
Implement a Breadth-First Search and Depth-First Search in Java to traverse decision trees.
Apply tree traversal algorithms such as BFS and DFS to generate rulesets from decision trees.
Skills you'll gain
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Recently updated!
December 2025
Assessments
1 assignment
Taught in English
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There are 3 modules in this course
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.
What's included
4 videos2 readings1 peer review
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.
What's included
3 videos1 reading1 peer review
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.
What's included
4 videos1 reading1 assignment2 peer reviews
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