r/MachineLearning - [P] Predict figure skating world championship ranking from season performances (part 2: hybrid models learned by gradient descent)

I previous posted the write-up on the first part of my project (Github repo) to predict how skaters would rank in the figure skating world championship from earlier scores that they earned in the season. The main idea is to separate the skater effect, the intrinsic ability of each skater, from the event effect, the influence of an event on a skater's performance, so that a more accurate ranking could be built. Unfortunately, this model does not have a closed-form solution to learn the parameters as opposed to the earlier models. Therefore, gradient descent was used to learn them, which resulted in this neat little animation that tracks how the model residuals, RMSE, as well as predicted ranking gets better and better as gradient descent runs. I also explore different strategies to reduce model overfit (so that it can predict skater ranking more accurately), using familiar methods such as model penalization and early stopping. Lastly, note that this hybrid model is nothing but factorizing the event-skater score matrix into an event-specific vector and skater-specific vector, which can multiply together to approximate the score matrix.