decision tree

Terms from Artificial Intelligence: humans at the heart of algorithms

Page numbers are for draft copy at present; they will be replaced with correct numbers when final book is formatted. Chapter numbers are correct and will not change now.

A decision trees represents complex rules as a series of simple conditions with sub-trees (branches) for further conditons depending in the outcome to the inital condition. Decsion trees can be hand crafted (e.g. decison trees in IVR), but most commonly encountered in AI as the output of machine learning algorithms such as Quinlan's ID3 or C4.5, or as part of a random forest.

Used in Chap. 5: pages 74, 80; Chap. 6: page 93; Chap. 8: page 119; Chap. 9: pages 127, 128, 131, 132, 134, 135; Chap. 10: page 146; Chap. 16: pages 266, 267; Chap. 18: page 302; Chap. 19: page 318; Chap. 21: pages 353, 356, 357, 360, 361; Chap. 24: page 400

Also known as decision trees

A simple decision tree