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Decision node

As explained in Grokking Machine Learning

An internal decision-tree node that represents a yes-or-no question and has two outgoing branches.

A decision node represents a yes-or-no question in a decision tree. Its two branches correspond to the answers and lead to another decision node or a leaf, where the tree’s decision is made. The topmost decision node is the root; a tree with just one decision node and two leaves is a decision stump. section 9.0

Choosing whether to wear a jacket

Ask whether it is raining. If yes, wear a jacket; if no, ask whether it is hot or cold, then decide. Each question is a decision node, and following the matching branches carries you through the tree. section 9.0

When building a classifier, a decision node splits the data using a feature. For example, splitting app users by age can create a node; a later split by platform can divide a mixed group further. section 9.2

Figure 9.1 A decision tree used to decide whether we want to wear a jacket or not on a given day. We make the decision by traversing the tree down and taking the branch corresponding to each correct answer.

Follow the branches in the jacket example to see how questions guide a decision through a tree. section 9.0

How Grokking Machine Learning covers it

How deep it goes
Defines two-way branching, then uses decision nodes in feature-based data splits; tree-building examples also address split metrics and stopping conditions.
Examples
Uses a jacket decision tree and app recommendations split first by age and then by platform.
What you can do after
Build a decision tree by recursively splitting labeled data on selected features until leaves are pure or a stopping condition applies.
How it connects
Connects decision nodes to decision trees, binary trees, root nodes, leaf nodes, decision stumps, and feature-based data splits.

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This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.