The following are some advantages from using random forests:

• More robust than just a single decision tree
• Random forests contain many decision trees and are therefore able to limit overfitting and error
• Depth-wise, the location shows which features contribute to the classification or regression as well as their relative importance
• Can be used for both regression and classification
• Default parameters can be sufficient
• Fast to train