What is a Random Forest?

A Random Forest is an ensemble method that builds many decision trees during training and aggregates their predictions. Each tree is trained on a random bootstrap sample of the data (bagging), and at each split, only a random subset of features is considered. This introduces diversity among trees, reducing variance while maintaining low bias. For classification, the forest takes a majority vote; for regression, it averages predictions. Random Forests are robust, handle high-dimensional data well, and provide feature importance scores.