Artificial Intelligence & Machine Learning
Random Forest
Definition
A Random Forest is an ensemble learning method used for classification and regression that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Why It Matters
Random Forest is a very powerful and versatile machine learning algorithm. By combining many "weak" decision trees, it corrects for the tendency of individual trees to overfit and generally produces a much more accurate and robust model.
Contextual Example
Instead of relying on a single decision tree to predict if a customer will churn, a company uses a random forest of 500 trees. The final prediction is determined by a "vote" from all the trees in the forest.
Common Misunderstandings
- The "random" part comes from the fact that each tree is trained on a random subset of the data and considers only a random subset of features at each split.
- It is a type of "bagging" ensemble method.