What is the difference between random forest and decision trees?

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On the fundamental level, how are RF and DT different?
Jul 30, 2018 in Data Analytics by Anmol
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The basic difference is that Random Forest (RF) is a collection or ensemble model of numerous Decision Trees (DT).

If you might ask why we go for Random Forest then reasons are listed below:

  1. One single DT would lead to over-fit model if the dataset is huge, same way like a single person might have its own perspective on the complete population.
  2. However if we implement the voting system and ask different individuals to interpret the data then we would be able to cover the patterns in a much meticulous way.
answered Jul 30, 2018 by ANMOL
• 3,620 points

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