Generalized Additive Models (GAMs)

Generalized Additive Models (GAMs) strike a rare balance: they combine the flexibility of complex models with the clarity of simple ones.

They often achieve performance comparable to black-box models, yet remain: - Easy to interpret - Computationally efficient - Aligned with the growing demand for transparency in AI

With recent U.S. AI regulations (White House, 2022) and increasing pressure from decision-makers for explainable models, GAMs are emerging as a natural choice across industries.

Audience

This guide is for readers with some background in Python and statistics, including: - Data scientists
- Machine learning engineers
- Researchers

Takeaway

By the end, you’ll understand: - The intuition behind GAMs
- How to build and apply them in practice
- How to interpret and explain GAM predictions and results in Python

Prerequisites

You should be comfortable with: - Basic regression concepts
- Model regularization
- The bias–variance trade-off
- Python programming