discontinuum#
GP’s are a flexible approach to machine learning, which are naturally suited for applications with sparse and noisy data or for uncertainty analysis. However, fitting GP’s is numerically expensive, which has led to a range of optimizations with different tradeoffs. Ideally, we could quickly write mathematical models, then run them on whichever “engine” is best suited for a particular problem.
Most models applications also include a fair amount of “boiler plate”
in the form of utility functions for plotting, managing metadata, data pre-processing, etc.
discontinum packages several engines and helper utilities into a single ecosystem
to simplify the process of prototyping GP models.