Day 117–126: Project — Improve a Previous Model with Hyperparameter Tuning
Module 3: Unsupervised & Reinforcement Learning Week 17–18 · Advanced ML & Course Review · Days 117–126
Today’s Agenda
By the end of this project you will have:
Revisited the XGBoost fraud detection model from Module 2 and established a reproducible baseline
Applied Bayesian hyperparameter optimization (Optuna) to systematically search a high-dimensional parameter space
Tracked every experiment, compared results with statistical rigor, and serialized the winning model as a production artifact
Understood the compute–accuracy tradeoff and built a reusable tuning pipeline you can drop into any future project
Why This Matters
Every ML model at Netflix, Uber, and Stripe starts life as a rough baseline — then gets systematically squeezed for every percentage point of performance. The difference between a 74% F1 fraud detector and an 83% one isn’t a cleverer algorithm or more data: it’s disciplined hyperparameter optimization. This 10-day project is where theory becomes practice. You’ll run a real search, track every trial, measure the gain with a held-out test set, and ship a provably better model. The baseline → experiment → validate → promote workflow you build here is the standard operating procedure inside every serious ML platform on the planet.
