Decision Tree Classifier

A decision tree classifier produces human-readable decision structures by recursively splitting the data according to rule-based conditions. One of its key advantages is that it makes it easy to visualize which features are most influential in separating classes. It can model nonlinear relationships and typically requires little feature scaling. However, deeply grown trees can overfit easily, so pruning, maximum depth, and minimum sample constraints are important controls.