#tree-based

A Decision Tree is a supervised learning algorithm used for both classification and regression tasks.

Gradient Boosting is an ensemble machine learning technique (boosting) that combines weak learners (typically shallow Decision Tree) sequentially to create a strong predictive model.

Random Forest is an ensemble learning method (bagging) that constructs a multitude of decision trees at training time and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.