In learning algorithms and statistical classification, a random forest is an ensemble classifier that consists in many decision trees. It outputs the class that is the mode of the classes output by individual trees, in other words, the class with the highest frequency.
Overview
Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees.
References
- Random Forest page maintained by Leo Breiman and Adele Cutler, the creators of the algorithm.
- Wikipedia pages on Random Trees, Random Forest and Ensemble Learning.
- The R CRAN page for the randomForest package is located here
Tag usage
Questions on tag random-forest should be about implementation and programming problems, not about the statistical or theoretical properties of the technique. Consider whether your question might be better suited to Cross Validated, the StackExchange site for statistics, machine learning and data analysis.
