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

[vishrut-singhal]

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.