Decision Tree

Ensemble Technique

Machine Learning

January 10, 2021

How prediction is made in Random Forest?

Random Forest works similar to bagging except for the fact that not all features(independent variables) are selected in a subset and that random Forest works only with Decision Trees. In bagging the subsets differ from original data only in terms of number of rows but in Random forest the subsets differ from the original data both in terms of number of rows as well as number of columns.

A tree is model is constructed for each subset and their results are aggregated. The method of aggregation depends upon the type of problem at hand.

  1. For classification problems, voting is used for aggregation
  2. For regression problems, mean/average is used for aggregation.

by : Monis Khan

Quick Summary:

Random Forest works similar to bagging except for the fact that not all features(independent variables) are selected in a subset and that random Forest works only with Decision Trees. In bagging the subsets differ from original data only in terms of number of rows but in Random forest the subsets differ from the original data […]