Decision trees are intuitive machine learning algorithms used for both classification and regression tasks. They work by recursively splitting data into subsets based on feature values, creating a tree-like structure that facilitates predictions. The structure consists of nodes, where the root node represents the initial decision point, and internal nodes represent subsequent decisions based on conditions. The final subsets, known as leaf nodes, provide the predicted outcomes ²⁵.

One of the key advantages of decision trees is their interpretability; they are easy to understand and visualize. They can handle both numerical and categorical data, require minimal preprocessing, and can model non-linear relationships. However, they can be prone to overfitting if not properly pruned, and small changes in the data can lead to different tree structures ²⁴.

Decision trees can be implemented using libraries like sklearn, which provides classes such as DecisionTreeClassifier and DecisionTreeRegressor for various applications ².