What Does Random Forest Mean?
Random forest is a consensus algorithm used in supervised machine learning (ML) to solve regression and classification problems. Each random forest is comprised of multiple decision trees that work together as an ensemble to produce one prediction.
A decision tree is a logical construct that resembles a flowchart and illustrates a series of if-else statements. An important purpose of using random forest is to compensate for the limitations of decision tree algorithms by mapping multiple trees and using the forest's average output (statistical mean).
Random forest algorithms can produce acceptable predictions even if individual trees in the forest have incomplete data. Statistically, increasing the number of trees in the ensemble will correspondingly increase the precision of the outcome.