Editing Decision tree (section)

== Advantages and disadvantages ==
Among decision support tools, decision trees (and [[influence diagrams]]) have several advantages. Decision trees:
* Are simple to understand and interpret. People are able to understand decision tree models after a brief explanation.
* Have value even with little hard data. Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes.
* Help determine worst, best, and expected values for different scenarios.
* Use a [[white box (software engineering)|white box]] model. If a given result is provided by a model.
* Can be combined with other decision techniques.
* The action of more than one decision-maker can be considered.
Disadvantages of decision trees:
* They are unstable, meaning that a small change in the data can lead to a large change in the structure of the optimal decision tree.
* They are often relatively inaccurate.  Many other predictors perform better with similar data.  This can be remedied by replacing a single decision tree with a [[random forest]] of decision trees, but a random forest is not as easy to interpret as a single decision tree.
* For data including categorical variables with different numbers of levels, [[information gain in decision trees]] is biased in favor of those attributes with more levels.<ref>{{cite conference |author=Deng, H.|author2=Runger, G. |author3=Tuv, E. |title=Bias of importance measures for multi-valued attributes and solutions |conference=Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN)|year=2011|url=https://www.researchgate.net/publication/221079908}}</ref>
* Calculations can get very complex, particularly if many values are uncertain and/or if many outcomes are linked.