Decision Tree is a machine learning algorithm that models decisions as a series of binary or multi-way splits. Each internal node represents a condition, branches represent outcomes, and leaf nodes indicate final predictions or classifications.

Key Features:

• Tree Structure
  Nodes = conditions, branches = outcomes, leaves = decisions.

• Interpretable Logic
  Easy to visualize and explain; considered a white-box model.

• Data Type Flexibility
  Handles both numerical and categorical variables.

• Non-Linear Modeling
  Captures complex patterns through recursive splits.

• Greedy Construction
  Built top-down using ID3, CART, or C4.5 based on impurity metrics.

Applications:

• Credit Scoring – Loan eligibility based on financial features

• Medical Diagnosis – Classifies symptoms into disease categories

• Churn Prediction – Assesses customer attrition risk

• Fraud Detection – Flags suspicious transactions

• Feature Selection – Highlights key inputs via split hierarchy

Why It Matters

Decision trees are intuitive and easy to interpret. Though prone to overfitting, they’re foundational for ensemble models like Random Forest and Gradient Boosted Trees—critical tools in real-world ML pipelines.