How and why to build your own gradient boosted-tree package

In order to make accurate and fast travel-time predictions, Lyft built a gradient boosted tree (GBT) package from the ground up. It is slower to train than off-the-shelf packages, but can be customized to treat space and time more efficiently and yield less volatile predictions. Machine learning runs at the core of what we do at Lyft. Examples include predicting travel time between two locations, modeling the probability of a ride being canceled, forecasting supply and demand, and many more. These models enable us to match riders and drivers more efficiently, incentivize drivers to be where they can get more rides, and improve the ride experience.