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Random Forest

Last updated: October 20, 2021

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.

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Techopedia Explains Random Forest

One way to describe the philosophy behind the random forest is that since the random trees have some overlap, engineers can build systems to study data redundantly with the various trees and look for trends and patterns that support a given data outcome.

For example, if five random trees provide information on the same variable from a subset, and four of them agree, the machine learning algorithm may utilize that “majority vote” to build models based on probabilities. In many different kinds of machine learning, constructs like the random forest can help technological systems to drill down into data and provide more sophisticated analysis.

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Data ManagementArtificial Intelligence Analytics/BIMachine LearningData Science

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