Random Forests provide deeper classification and better predictions Instead of creating a single decision tree, the Random Forest algorithm can create many individual trees from randomly selected ...

The random forest algorithms choose large numbers of random subsets of the data records and variables, construct a regression tree for each random subset, and let the resulting regression trees ...

The Annals of Statistics, Vol. 43, No. 4 (August 2015), pp. 1716-1741 (26 pages) Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45 (2001) 5-32] that combines several ...

Random Forests can be applied to machine learning — for example, with autonomous cars, what decision process should the algorithm apply if it is about to crash in order to minimise damage, or risk of ...

The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make "different" mistakes, i.e., have weakly correlated ...

Based on this research, Wei Ran Lab has conducted big data analysis, trained millions of samples, and selected the Random Forest algorithm to identify threats in encrypted communication traffic.