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Thanks for the article, it resonates strongly! I teach Data / AI courses regularly and this topic is a recurring one with students. We the instructors like to

Thanks for the article, it resonates strongly! I teach Data / AI courses regularly and this topic is a recurring one with students. We the instructors like to make students start without even ML in the first place for them to construct a baseline model, usually using simple heuristics (e.g. conditions, regexes, etc.). At first, students don't like it and tend to skip it because it is not that impressive, but in the end they learn so much about the data and the problem at hand! Then they are allowed to try simple ML models to see if that's a performance improvement and so on with more complex models. In some cases, improving baseline models with complex ML/DL models is really hard (e.g. time-series). Another benefit of simple models is explainability, for which the industry demand is growing everyday.

figurative 4y ago
Just out of curiosity where do you see the demand? I've also heard from others that there's been a shift lately towards simpler models. Combined with domain knowledge linear/logistic regression can be really impressive!
- dbs 4y ago
  > Combined with domain knowledge linear/logistic regression can be really impressive! Can you elaborate?
  - rmnclmnt 4y ago
    I can agree this the comment. Linear models combined with advanced feature engineering gathered from domain knowledge can achieve great results in a white-box fashion! A nice keynote by Vincent Warmerdam [1] talks about tips and tricking for advanced feature engineering combined with linear models. [1] https://www.youtube.com/watch?v=68ABAU_V8qI https://www.youtube.com/watch?v=68ABAU_V8qI
  - beckingz 4y ago
    A significant portion of ML workloads involve predicting or classifying something. Linear/logistic regression of the right variables/features typically gets a significant portion of the data's ability to predict /classify correctly, while being significantly easier to build, train, deploy, and understand. Heck, in a large number of domains, simple ratios -- debt to income ratio in finance for example -- will dominate the feature weight for many models and can be used on their own as a pretty good heuristic.
- rmnclmnt 4y ago
  Small non-FAANG companies usually, where they do not have the internal skills to maintain and explain models. And from what I've heard, big corporations under regulated domains (banking, healthcare, etc).

figurative 4y ago

Just out of curiosity where do you see the demand? I've also heard from others that there's been a shift lately towards simpler models. Combined with domain knowledge linear/logistic regression can be really impressive!

dbs 4y ago
> Combined with domain knowledge linear/logistic regression can be really impressive! Can you elaborate?
- rmnclmnt 4y ago
  I can agree this the comment. Linear models combined with advanced feature engineering gathered from domain knowledge can achieve great results in a white-box fashion! A nice keynote by Vincent Warmerdam [1] talks about tips and tricking for advanced feature engineering combined with linear models. [1] https://www.youtube.com/watch?v=68ABAU_V8qI https://www.youtube.com/watch?v=68ABAU_V8qI
- beckingz 4y ago
  A significant portion of ML workloads involve predicting or classifying something. Linear/logistic regression of the right variables/features typically gets a significant portion of the data's ability to predict /classify correctly, while being significantly easier to build, train, deploy, and understand. Heck, in a large number of domains, simple ratios -- debt to income ratio in finance for example -- will dominate the feature weight for many models and can be used on their own as a pretty good heuristic.
rmnclmnt 4y ago
Small non-FAANG companies usually, where they do not have the internal skills to maintain and explain models. And from what I've heard, big corporations under regulated domains (banking, healthcare, etc).