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Comment by tehsauce | Hacker News Reader

Curious, any specific examples of missing knowledge or misconceptions?

I tend to notice that model evaluation is lacking, perhaps because it’s not especially interesting.

But to me, most business applied ML falls under the optimization umbrella. For some reason it’s never portrayed this way, but perhaps if it were, junior practitioners would more commonly pay attention to learning to thoroughly examine how their trained models will perform.

Last year I nerded out on model accuracy and it was great fun. I would love to do this more at work but unless I fall into it in an existing role I usually am not considered for data science roles because I don't have 7 Ph.Ds. Simple accuracy, R2 for classification, ROC curves, calibration, Brier Skill scores. Good times.

The best book I found at the time was Kuhn & Johnson. If anyone can recommend a better book I'd love to hear it? (Examples in R or Python, it doesn't matter.)

I don't know what type of ML you do, but my experience is that it is hard to pin point poor model evaluation as being the reason for poor performance in production (especially if you have an interest in protecting your ego). It can be hard enough to figure out if you even have poor performance in production as you often don't know how well you could/should be doing. I have recently started thinking about what part of the performance issues I have are due to poor evaluation vs under-specification. I'm not sure how I'm supposed to tease the two apart.

Misconceptions: not knowing regression vs classification, supervised vs unsupervised, or thinking that ML is just neural networks.

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