Forecasting
Predicting Rider Conversion in Sparse Data Environments with Bayesian Trees
Our short summary of Lyft’s engineering write-up. The full story, with all the detail, is on their blog.
Read the full write-up on eng.lyft.com (opens in a new tab)Problem
Rider conversion rates were hard to predict because data for specific contexts was extremely sparse.
Solution
They built a Bayesian tree model that shares information across similar contexts, avoiding over‑fitting and running fast enough for live predictions.
Want the architecture, code and numbers? They’re in the original post.
Read the full write-up on eng.lyft.com (opens in a new tab)