surely, Relation between infinite KeRF and infinite random forest When the number of trees goes to infinity, then we have infinite random forest and infinite KeRF. Their estimates are close if the number of observations in each cell is bounded: Assume that there exist sequences such that, almost surely Then almost surely, Consistency results Assume that , where is a centered Gaussian noise, independent of , with finite variance . Moreover, is uniformly distributed on and is Lipschitz. Scornet proved upper bounds on the rates of consistency for centered KeRF and uniform KeRF. Consistency of centered KeRF Providing and , there exists a constant such that, for all , . Consistency of uniform KeRF Providing and , there exists a constant such that, . Disadvantages While random forests often achieve higher accuracy than a single decision tree, they sacrifice the intrinsic interpretability present in decision trees. Decision trees are among a fairly small family of machine learning models that are easily interpretable along with linear models, rule-based models, and attention-based models. This interpretability is one of the most desirable qualities 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. 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. If it is established that the predictive attributes are linearly correlated with the target variable, using random forest may not enhance the accuracy of the base learner. Furthermore, in problems with multiple categorical variables, random forest may not be able to increase the accuracy of the base learner. See also References Further reading External links Random Forests classifier description (Leo Breiman's site) Liaw, Andy & Wiener, Matthew "Classification and Regression by randomForest" R News (2002) Vol. 2/3 p. 18 (Discussion of the use of the random forest package for R) Classification algorithms Ensemble learning Decision trees Decision theory Computational statistics