Pedro Albuquerque
Hi everyone — I’m Pedro Albuquerque, Principal Data Scientist at AppOrchid. I work where machine learning, econometrics, and applied research meet, with a big focus on interpretable, trustworthy AI. Over the past 15+ years I’ve built and shipped data products in industry (AppOrchid, FleetOps, Convoy, ServiceNow/ElementAI) and stayed active in academia (2,000+ citations, multiple peer-reviewed papers). I’ve also taught in math, CS, and business departments and founded a lab that mentored 30+ students on ML for finance, business, and social impact.
Session
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