Applications of Random Forest classifier and Advantages and Disadvantages of Random Forest Classifier

Applications of Random Forest

There are mainly four sectors where Random forest mostly used:

  1. Banking: Banking sector mostly uses this algorithm for the identification of loan risk.
  2. Medicine: With the help of this algorithm, disease trends and risks of the disease can be identified.
  3. Land Use: We can identify the areas of similar land use by this algorithm.
  4. Marketing: Marketing trends can be identified using this algorithm.

Advantages of Random Forest

  • Random Forest is capable of performing both Classification and Regression tasks.
  • It is capable of handling large datasets with high dimensionality.
  • It enhances the accuracy of the model and prevents the overfitting issue.

Disadvantages of Random Forest

  • Although random forest can be used for both classification and regression tasks, it is not more suitable for Regression tasks.