- Tags:: #📜Papers * Author:: [[Shreya shankar|Shreya Shankar]] * Link:: [Predictive Modeling: A Retrospective (shreya-shankar.com)](https://www.shreya-shankar.com/8d5c6ec070babe7c23d3d5b68384a8bd/retrospective.pdf) (Google Brain, Facebook) * Source date:: [[2021-01-08]] * Finished date:: [[2021-06-13]] The typical paper that realizes that impact comes from good selection of features working with domain experts. Contradicting what is was said in [[Range|Range]]: >To be frank, **I don't think we can benefit from domain expertise too much...** It's very hard to win a competition just by using well known methods. We need more creative solutions. Also, two modes of experimentation. A very hacky mode, but also, as indicated in [[How to lead in data science|How To Lead In Data Science]]: >So I learned the second mode, the slow mode, when I needed to show results to other people. I deploy the pipeline with the DAG scheduler, version everything – data, model, code, artifacts, you name it – and see if I can replicate results multiple times through cross validation. I never trust my results or communicate them to someone else until I’ve seen results of the second mode. I am rigorous in this mode; I put my software engineering hat on and get to work. It is tedious, but if there is anything I have learned about data science, it is that I need to be disciplined and patient in order to repeatedly reap the benefits of this sorcery (p. 14)