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How do you build a random forest model?

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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:

  1. Randomly select 'k' features from a total of 'm' features where k << m
  2. Among the 'k' features, calculate the node D using the best split point
  3. Split the node into daughter nodes using the best split
  4. Repeat steps two and three until leaf nodes are finalized 
  5. Build forest by repeating steps one to four for 'n' times to create 'n' number of trees 

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