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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
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Depends on the market. My market case study is easy. What matters for us is that you can back up the algorithms/techniques you use or mention. We don’t judge on how fancy the solution is. For instance, don’t say you can do NNs if you’re not prepared to discuss overfitting strategies, architectures, or deployments. Stick to what you know (even if it’s just logistic regression), and be curious about what you don’t.