Random Forest operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. It enhances the decision tree method by adding randomness in two ways: each tree is built from a random sample of the data, and at each split in the tree, a random subset of the features is considered. This randomness helps to make the model more robust against overfitting, and the ensemble nature of Random Forest generally leads to better performance than individual decision trees in many scenarios. Random Forests also provide useful insights into feature importance, which can be instrumental in understanding input variables' predictive power.
Historical Overview: The concept of Random Forest was first introduced by Leo Breiman and Adele Cutler in 1995. The algorithm gained significant popularity after Breiman published a more refined version of the algorithm in 2001, highlighting its advantages in terms of accuracy and robustness compared to other methods.
Key Contributors: Leo Breiman, a statistician at the University of California, Berkeley, was the principal developer of the Random Forest algorithm. His work, along with contributions from Adele Cutler, has been pivotal in the development and popularization of ensemble methods in machine learning. Their collaborative efforts have significantly shaped the way Random Forests are understood and applied in various predictive modeling tasks.