Binary tree
A binary tree is a tree data structure in which each node has at most two children.
Grokking Machine Learning
A tree data structure in which each node has at most two child branches.
- How it is used
- Represents yes-or-no decision processes as branches leading to decisions.
- How deep it goes
- Defines roots, decision nodes, leaves, branches, and tree depth.
- Examples
- Uses rain and temperature questions to decide whether to wear a jacket.
- What you can do after
- Apply branch traversal to follow yes-or-no questions toward a decision.
Grokking Algorithms, Second Edition
A binary tree is a tree in which each node has at most two children.
- How it is used
- Provides the two-child tree structure used to introduce Huffman coding and binary search trees.
- How deep it goes
- Defines the node limit and subtrees, then works through a search-tree example without implementing a general binary tree.
- Examples
- Uses an ancestry chart and a search for 7 in a binary search tree rooted at 10.
- What you can do after
- Evaluate/understand whether a tree meets the two-child limit and why a binary search tree can skip branches during search.
Read how Grokking Algorithms, Second Edition explains binary tree →
Learn C++ by Example
A binary tree is a tree data structure in which each node has at most two child branches.
- How it is used
- Uses binary trees to explain how map searches avoid scanning every stored element.
- How deep it goes
- Explains nodes, child pointers, ordering, balancing, and one search path, but stops short of implementation code.
- Examples
- Uses map pairs {1:a}, {3:c}, and {5:e}, plus a search for {2:b}, to demonstrate insertion and traversal.
- What you can do after
- Evaluate how map ordering and balancing reduce search work compared with linear container scans.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A tree data structure in which each node has at most two child nodes.
- How it is used
- Uses binary trees to partition data while training gradient-boosting models.
- How deep it goes
- Explains feature scanning, threshold evaluation, objective scoring, and histogram acceleration, but not complete tree construction.
- Examples
- Demonstrates exhaustive and 256-bin histogram splits on latitude data using Python listings.
- What you can do after
- Diagnose split-search costs and apply histogram-based candidate reduction for large numeric datasets.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains binary tree →
Related concepts
- treekind of · A tree is a connected, acyclic structure of nodes, commonly organized hierarchically through parent-child relationships and optionally around a designated root.
- binary search treeincludes · A binary search tree is a binary tree whose nodes follow an ordering relation placing keys in designated left and right subtrees relative to each node.
- heapincludes · A binary heap is an almost complete binary tree in which each parent satisfies an ordering property relative to its children, enabling efficient access to a minimum or maximum element.
- k-d treeincludes · A k-d tree is a binary space-partitioning tree that organizes points in multidimensional space by recursively comparing coordinate dimensions.
- subtreeincludes · A subtree is a tree formed by a node and all its descendants, together with the links connecting them.
- Mapcontrasts with · A map is an associative data structure that stores key-value pairs and supports retrieving values by key.
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