A Decision Tree is a supervised machine learning algorithm used for both classification and regression tasks. It operates by creating a model that predicts the value of a target variable based on several input features. The structure of a decision tree resembles a tree, 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.

In classification tasks, decision trees categorize data into discrete classes. For example, they can classify whether an email is spam or not based on features like the presence of certain keywords. In regression tasks, they predict continuous values, such as estimating house prices based on various attributes like size and location ¹⁴.

The process of building a decision tree involves selecting the best feature to split the data at each node, using metrics like entropy, information gain, or Gini impurity to determine the optimal splits. This recursive partitioning continues until a stopping criterion is met, resulting in a model that can make predictions on new data ²⁵.