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Random forests

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This article describes the Random Forest (RF) learning method, a combination of tree predictors, each grown on a subset (sampling with replacement) of the original training set. The article mostly focuses on classification’s problems: each tree makes its prediction, i.e., votes for a class, and the most voted class is chosen as prediction of the forest for that instance. The paper shows with case studies that growing an ensemble of trees results in significant improvements in classification accuracy respect to single trees and obtain good results also respects to Adaboost.
First, the article gives some theoretical background for RFs and then carry out different examples to reinforce and explore the theoretical concepts. In particular, it focuses on two hot topics: how many features have to be randomly selected at each node to determine the split and the relationship between strength of the individual trees in the forest and correlation between trees.

Scientific Paper

Data Analytics, Machine Learning

Target Group:


Cite as:
Breiman, L., Random forests, Machine learning 45.1 (2001): 5-32.

Author of the review:
Giulia Cademartori
University of Genoa


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Pablo Guerrero-Garcia

I was wondering whether this article also describes explicitly the application of random forests to develop a recommender system (RS). In such a case, it would be nice to also get a metric as could be RMSE or MAE for their throughtput to be directly comparable with that of collaborative filtering RSs.

Antonio CinĂ 

This article clearly explains Random Forest algorithm, how it works and its most important parameters, including case studies and examples to help the reader better understand the concepts.