Editing Decision tree (section)

== Overview ==

A decision tree is a [[flowchart]]-like structure in which each internal node represents a test on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent [[classification]] rules.

In [[decision analysis]], a decision tree and the closely related [[influence diagram]] are used as a visual and analytical decision support tool, where the [[expected value]]s (or [[expected utility]]) of competing alternatives are calculated.

A decision tree consists of three types of nodes:<ref>{{Cite journal | doi = 10.1007/s10100-017-0479-6| pmid = 29375266| pmc = 5767274| title = A framework for sensitivity analysis of decision trees| journal = Central European Journal of Operations Research| volume = 26| issue = 1| pages = 135–159| year = 2017| last1 = Kamiński | first1 = B. | last2 = Jakubczyk | first2 = M. | last3 = Szufel | first3 = P.}}</ref>
# Decision nodes – typically represented by squares
# Chance nodes – typically represented by circles
# End nodes – typically represented by triangles

Decision trees are commonly used in [[operations research]] and [[operations management]]. If, in practice, decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a [[probability]] model as a best choice model or online selection model [[algorithm]].{{Citation needed|date=July 2021}} Another use of decision trees is as a descriptive means for calculating [[conditional probability|conditional probabilities]].

Decision trees, [[influence diagrams]], [[utility function]]s, and other [[decision analysis]] tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or [[management science]] methods. These tools are also used to predict decisions of householders in normal and emergency scenarios.<ref>{{Cite journal |last1=Xu |first1=Ningzhe |last2=Lovreglio |first2=Ruggiero |last3=Kuligowski |first3=Erica D. |last4=Cova |first4=Thomas J. |last5=Nilsson |first5=Daniel |last6=Zhao |first6=Xilei |date=2023-03-01 |title=Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire |url=https://doi.org/10.1007/s10694-023-01363-1 |journal=Fire Technology |language=en |volume=59 |issue=2 |pages=793–825 |doi=10.1007/s10694-023-01363-1 |issn=1572-8099|url-access=subscription }}</ref><ref>{{Cite journal |last1=Díaz-Ramírez |first1=Jenny |last2=Estrada-García |first2=Juan Alberto |last3=Figueroa-Sayago |first3=Juliana |date=2023-12-01 |title=Predicting transport mode choice preferences in a university district with decision tree-based models |journal=City and Environment Interactions |volume=20 |pages=100118 |doi=10.1016/j.cacint.2023.100118 |issn=2590-2520|doi-access=free |bibcode=2023CEnvI..2000118D }}</ref>