Random Forest or Random Decision Forest is a method that operates by constructing multiple decision trees during the training phase. The decision of a majority of the trees is chosen by the random forest as the final decision. The reasons why is this method more effective than others are :

  1. No Overfitting: It uses multiple trees to reduce the risk of overfitting. Also, the training time is less.
  2. High Accuracy: It runs efficiently on a large dataset and for a large dataset, it produces highly accurate predictions.
  3. Estimates Missing data: It can maintain accuracy when a large portion of data is missing.

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