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[D] Why are ML model outputs not tested regarding statistical significance? (alien.top)

submitted 1 year ago by Tigmib@alien.top to c/machinelearning@academy.garden

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Often when I read ML papers the authors compare their results against a benchmark (e.g. using RMSE, accuracy, ...) and say "our results improved with our new method by X%". Nobody makes a significance test if the new method Y outperforms benchmark Z. Is there a reason why? Especially when you break your results down e.g. to the anaylsis of certain classes in object classification this seems important for me. Or do I overlook something?

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[–] bethebunny@alien.top 1 points 1 year ago (1 children)

While it's not super common in academia, it's actually really useful in industry. I use statistical bootstrapping -- poisson resampling of the input dataset -- to train many runs on financial fraud models and estimate variance of my experiments as a function of sampling bias.

Having a measure of the variance of your results is critical when you're deciding whether to ship models whose decisions have direct financial impact :P

[–] Lanky_Product4249@alien.top 1 points 1 year ago

Does it actually work? I.e. if you construct a 95% confidence interval with that variance, are your model predictions within the interval 95% of the time?