7. Random Forest in R programming language

Some steps for building a random forest

  • Randomly select “K” features from total “m” features where k < m
  • Among the “K” features, calculate the node “d” using the best split point
  • Split the node into daughter nodes using the best split method
  • Repeat the previous steps until you reach the “l” number of nodes
  • Build a forest by repeating all steps for “n” number times to create “n” number of trees

After the random forest trees and classifiers are created, predictions can be made using the following steps:

  • Run the test data through the rules of each decision tree to predict the outcome and then store that predicted target outcome
  • Calculate the votes for each of the predicted targets
  • The most highly voted predicted target is the final prediction 

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