Decision Tree Classifier Explained

A decision tree classifies data by asking a series of yes-or-no questions, splitting the data step by step until it reaches a final decision.

Each question represents a node, and each possible answer leads to another question or an outcome, forming a tree-like structure.

For instance, a decision tree predicting loan approval might first ask if income is above a certain amount, then ask about credit score, narrowing down to a final approve or reject decision.

• The tree structure makes decisions easy to visualize and explain.
• Deeper trees can capture more detail but risk overfitting.
• Decision trees form the foundation for more advanced models like random forests.

Because of their clear, rule-based structure, decision trees are often used when explainability matters as much as accuracy.