Decision Tree

A decision tree begins with a root node and repeatedly splits data based on selected features or conditions. Each branch represents the outcome of a decision, while the final leaf nodes represent the model’s prediction. Decision trees can handle both classification and regression tasks and are often easy to visualize and interpret.

Decision trees provide a relatively transparent way to understand how a model reaches its predictions. They can capture non-linear relationships, work with different types of data, and require relatively little preprocessing. However, individual trees can overfit training data if they become too complex.