double descent is a thing

Yesterday in the Flatiron Astronomical Data Group weekly meeting, I showed the crew something called double descent: When you are training a model and the number of data points you have (number of training-set objects) approaches the number of features you have (number of pixels in your image, say) then regression models often blow up. That is, you get better answers with fewer training objects when the number approaches certain values. This is a highly discussed issue in machine learning (and math and statistics; it's like some kind of phase transition) but hasn't really hit the domains (like astronomy) very much yet. The crew was surprised so today I made a tiny colab notebook to demonstrate it for a polynomail fit. It's amusing!