Decision Tree
Decision Tree is a supervised machine learning algorithm used for classification and regression tasks, which models decisions and their possible consequences as a tree-like structure of nodes and branches. It recursively splits data based on feature values to create homogeneous subsets, with internal nodes representing features, branches representing decision rules, and leaf nodes representing outcomes. This algorithm is intuitive to interpret and visualize, making it popular for explaining model predictions.
Developers should learn Decision Tree algorithms when building interpretable machine learning models for tasks like customer segmentation, fraud detection, or medical diagnosis, where understanding the decision-making process is crucial. It is particularly useful for handling both numerical and categorical data, and serves as a foundation for ensemble methods like Random Forest and Gradient Boosting, which improve performance by combining multiple trees.