Last semester, I learned about Gaussian Processes. They seemed really intriguing at the first glance, and it turned out they are even more intriguing as you dig deeper. This post is an application-oriented intro to Gaussian Processes. I’ll cover GP regressions, forecasting for time series and usage of GPs in bayesian optimization among other things.
Log-transformations and their interpretation as percentage impact is taught in every introductory regression class. But are most people aware that there is a hidden approximation behind the percentage-based intuition? One that may not be appropriate in some cases?