Decision Trees and Ensemble Methods

Introduction

A decision tree is a model that makes predictions by asking a series of yes/no questions about the input features, splitting the data at each step until it reaches a decision. Its structure is easy to read and interpret, which makes decision trees popular when explainability matters. They handle both classification and regression tasks.

Definition

A decision tree is a tree-like model that makes decisions based on asking a series of questions about the input features.

Types

Classification Trees

Trees that predict categorical outcomes

Regression Trees

Trees that predict continuous values

Random Forests

Ensemble of decision trees for improved performance

Gradient Boosting

Sequential ensemble method that builds on previous trees

Use Cases

• Medical diagnosis systems
• Credit scoring
• Customer segmentation
• Risk assessment
• Feature importance analysis

Implementation

Decision trees are prone to overfitting, which is why ensemble methods like Random Forest are often preferred.

In Practice

Trees split data to maximize purity using measures like Gini impurity or information gain. Single trees can overfit, so ensembles such as random forests and gradient-boosted trees combine many trees to improve accuracy and stability while keeping much of the modeling power.

Key Points

• Easy to interpret and visualize
• Can handle both numerical and categorical data
• Prone to overfitting on complex datasets
• Ensemble methods improve performance and robustness

References

• Decision Trees in Scikit-learn — Comprehensive guide to decision trees and ensemble methods