A Decision Tree is a supervised machine learning algorithm used for both classification and regression tasks. It operates by creating a tree-like model where each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome or class label. This structure allows the algorithm to make decisions based on the input features, effectively mimicking human decision-making processes.

There are two main types of Decision Trees: those that handle categorical target variables and those that handle continuous target variables. Categorical Decision Trees classify data into discrete categories, while Continuous Decision Trees predict continuous outcomes, such as prices or measurements. The algorithm is particularly useful because it can handle both categorical and numerical data, making it versatile for various applications ¹⁴⁵.

Decision Trees are favored for their simplicity and interpretability, allowing users to understand the decision-making process easily. However, they can become complex and overfit the training data if not managed properly.