Decision trees are a machine learning algorithm that predicts the value of a target variable based on decision rules learned from training data. The algorithm can be applied to both regression and classification problems by changing the objective function that governs how the tree learns the decision rules.

We will discuss how decision trees use rules to make predictions, how to train them to predict (continuous) returns as well as (categorical) directions of price movements, and how to interpret, visualize, and tune them effectively.