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There is a trend in machine learning implementations to make things easier and easier for implementers, a very natural engineering concern. Easy APIs to create any kind of model you want, easy infrastructure to manage versions of data and models, easy deployment of models as APIs. One of these trends is AutoML, an end-to-end process of creating a model (out of many) on very few general hyperparameters, hiding more and more of the usual stats process, all to the point of reducing the need for understanding the many hard to learn nuances of the statistical practices involved.

On the whole other end of the spectrum are the methods to addresse replicability crisis occurring in many scientific areas, mostly motivated by the poor use of statistics: confusion of statistical and effect significance, p-hacking, HARK-ing, other superficial uses of statistics. All this is asking people who use these tools to know more and better the nuances of statistical thinking.

Details are missing about the innards of AutoML: is it running an SVM and a LR and a RF with multiple kernels, hyperparams, etc? Is it following basic defensive statistics like Bonferroni correction? Or is it just jumping straight in to picking he best p-value out of all?

I've set this up as a dichotomy between ease of use in engineering and the correct thought in the statistical procedures. AutoML seems like a great thing for creating successful models. But then I wonder if they're not only ignoring the entire history of statistical thinking but even running away from it.

Are the AutoML researchers taking into account the statistical nuances successfully or are they enabling even more problems with models by ignoring the nuances (choosing between too many models for the amount of data)? And likewise are those who are statisticians making it harder to make reputable models? As a side question, is this characterization of AutoML as a more problematic statistical procedure accurate?

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  • $\begingroup$ AutoML is generally used for finding a model, or possibly the best model, for a given fixed task. This breaks down into data preprocessing, feature selection, model selection and hyper parameter tuning. This doesn't really have anything to do with p-values or Bonferroni corrections, as the goal is to make the most accurate, or fastest, model, sometimes without any human supervision involved. Whereas what you're asking about sounds more like experimental design. $\endgroup$ – Alex R. Mar 11 at 19:24
  • $\begingroup$ @AlexR. Not that AutoML does this, but isn't choosing a model with a lower p-value one way to attempt to find a better model? $\endgroup$ – Mitch Mar 11 at 20:59
  • $\begingroup$ Model choice is based on a metric related to accuracy, precision, ranking, etc. $p$-values would enter here if you trained 100 models, the question is, is the best model actually better than the rest or did it get lucky on the validation set. So there's going to be some statistics involved in correctly interpreting cross-validation results. To your credit, the analysis tends to be thin because it's expensive to train over multiple folds of data. So there's probably some p-hacking going on in published ML research (you wouldn't publish a paper with worse results than the current best model). $\endgroup$ – Alex R. Mar 11 at 21:36

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