A random forest is built up of a number of decision trees. If you split the data into different packages and make a decision tree in each of the different groups of data, the random forest brings all those trees together.
Steps to build a random forest model:
- Randomly select 'k' features from a total of '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
- Repeat steps two and three until leaf nodes are finalized
- Build forest by repeating steps one to four for 'n' times to create 'n' number of trees
