• An ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees. -Wikipedia

Is a:

• method

Applications:

• Classification
• Regression

Advantages:

• Corrects for decision trees habit of overfitting their training sets
• Robust to outlying observations
• generally outperforms decision trees -Wikipedia

Disadvantages:

• not interpretable