Editing Random forest (section)

== Disadvantages ==
While random forests often achieve higher accuracy than a single decision tree, they sacrifice the intrinsic [[interpretability]] of decision trees. Decision trees are among a fairly small family of machine learning models that are easily interpretable along with linear models, [[rule-based machine learning|rule-based]] models, and [[attention (machine learning)|attention]]-based models. This interpretability is one of the main advantages of decision trees. It allows developers to confirm that the model has learned realistic information from the data and allows end-users to have trust and confidence in the decisions made by the model.<ref name=":0" /><ref name="elemstatlearn" /> For example, following the path that a decision tree takes to make its decision is quite trivial, but following the paths of tens or hundreds of trees is much harder. To achieve both performance and interpretability, some model compression techniques allow transforming a random forest into a minimal "born-again" decision tree that faithfully reproduces the same decision function.<ref name=":0" /><ref>{{Cite journal|last1=Sagi|first1=Omer|last2=Rokach|first2=Lior|date=2020|title=Explainable decision forest: Transforming a decision forest into an interpretable tree.|url=https://www.sciencedirect.com/science/article/pii/S1566253519307869|journal=Information Fusion |language=en|volume=61|pages=124–138|doi=10.1016/j.inffus.2020.03.013|s2cid=216444882}}</ref><ref>{{Cite journal|last1=Vidal|first1=Thibaut|last2=Schiffer|first2=Maximilian|date=2020|title=Born-Again Tree Ensembles|url=http://proceedings.mlr.press/v119/vidal20a.html|journal=International Conference on Machine Learning|language=en|publisher=PMLR |volume=119|pages=9743–9753|arxiv=2003.11132}}</ref>

Another limitation of random forests is that if features are linearly correlated with the target, random forest may not enhance the accuracy of the base learner.<ref name=":0" /><ref name=":1" /> Likewise in problems with multiple categorical variables.<ref name=":3">{{Cite thesis|title=The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads (Doctoral dissertation)|date=November 2019|url=https://tspace.library.utoronto.ca/handle/1807/97601|type=Thesis|last1=Piryonesi|first1=Sayed Madeh}}</ref>