Random Forest

In general, decision trees are popular for machine learning tasks. In a random forest, engineers construct sets of random decision trees to more carefully isolate knowledge from data mining, with different applied variable arrays. 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.