Decision Trees
Decision Trees are a supervised machine learning algorithm used for classification and regression tasks, structured as a tree-like model where internal nodes represent features, branches represent decision rules, and leaf nodes represent outcomes. They recursively split data based on feature values to create a model that predicts target variables by following a path from the root to a leaf. This method is intuitive and interpretable, making it popular for data analysis and predictive modeling.
Developers should learn Decision Trees when working on projects requiring interpretable models, such as in finance for credit scoring, healthcare for disease diagnosis, or marketing for customer segmentation, as they provide clear decision rules and handle both numerical and categorical data. They are also useful as a baseline for ensemble methods like Random Forests and Gradient Boosting, and in scenarios where model transparency is critical for regulatory compliance or stakeholder communication.