Decision Tree

A decision tree is one of the fundamental machine learning algorithms that performs classification or regression by repeatedly branching based on conditions derived from data features. It has a tree structure (root node branching into leaf nodes), starting from the root and following branches to arrive at a final prediction (a leaf node). Key characteristics of decision trees: • Model structure is visual and intuitively understandable (white-box model) • Requires minimal preprocessing; handles categorical variables and missing values with ease • Can achieve high-accuracy classification and regression even on small datasets, while controlling overfitting • Once built, the tree can be used to derive rule-based decisions For classification problems, the output is a class label; for regression problems, it is a numerical prediction. Common metrics used for branching include Gini impurity, Information Gain, and Mean Squared Error (MSE). Example use cases: • Customer purchase and churn behavior analysis • Disease prediction from medical data • Numerical forecasting of sales or demand • Recommendation systems with filtering and conditional branching Single decision trees are sensitive to noise and prone to overfitting, which is why they are often combined with ensemble methods like Random Forest or XGBoost. That said, decision trees remain a valued algorithm in business and education for their transparency and interpretability.