The decision tree model in machine learning is an algorithm that offers choices based on characteristics of the data. It follows 'branch node theory' in which each branch will represent a variable alongside a decision.
Often decision tree models will be expressed in the following rule format:
- IF variable = x AND variable = y THEN outcome = z
There are different types of decision tree model algorithms. Some of these algorithms have been used for radiology image classification and thus appear the radiology literature ( including CART 1 and CHAID 2 ) or radiology related computer science literature ( ID3 3,4). In contrast to many methods of machine learning, decision tree models utilize heuristics and are therefore comprehensible.
While classically visualized by diagrams of connected nodes, decision tree models can be visualized by partitioned graphs or perspective plots.
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