Random Forest Classifier: An In-depth Overview
by: Akhil Kancherla Introduction Random forests, also known as random decision forests, are a powerful ensemble learning method used for classification, regression, and other tasks. This method operates by constructing a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Understanding Random Forests Random forests correct for decision trees' tendency to overfit to their training set. They generally outperform single decision trees, but their accuracy might be lower than gradient boosted trees. However, the performance can vary based on data characteristics. The first algorithm for random decision forests was created in 1995 by Tin Kam Ho using the random subspace method. This method is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg. An ...