Random Forest Classifier

Random Forest Classifier is a specialized type of tree-based model implementing meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

Advantages:

• It performs well on imbalanced datasets
• It is robust to outliers
• There is more generalization and less overfitting
• It is useful to extract feature importance

Disadvantages:

• It requires that features need to have some predictive power