Explain the Random Forest Model. How Do You Build a Random Forest Model?

Data Science, AI and ML
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A random forest model is a machine learning algorithm and a form of supervised learning. It is used most commonly in regression and classification problems. Here are the steps to build a random forest model:

  1. From a dataset with k records, select n.
  2. Construct individual decision trees for each of the n data values under consideration. A predicted result is obtained from each of them.
  3. A voting algorithm is applied to each of the results.
  4. The prediction with the most votes is assigned as the final result.