Your data-driven claims must still be probed severely

Vagelos Education Center

Below are the slides from my talk today at Columbia University at a session, Philosophy of Science and the New Paradigm of Data-Driven Science, at an American Statistical Association Conference on Statistical Learning and Data Science/Nonparametric Statistics. Todd was brave to sneak in philosophy of science in an otherwise highly mathematical conference.

Philosophy of Science and the New Paradigm of Data-Driven Science : (Room VEC 902/903)
Organizer and Chair: Todd Kuffner (Washington U)

  1. Deborah Mayo (Virginia Tech) “Your Data-Driven Claims Must Still be Probed Severely”
  2.  Ian McKeague (Columbia) “On the Replicability of Scientific Studies”
  3.  Xiao-Li Meng (Harvard) “Conducting Highly Principled Data Science: A Statistician’s Job and Joy

 

Categories: slides, Statistics and Data Science | 5 Comments

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5 thoughts on “Your data-driven claims must still be probed severely

  1. Posting the full thread that I originally posted on Twitter (
    https://twitter.com/orestistsinalis/status/1004097202887757824)

    In data science, it is frequently the case that the metric that is being optimised in an ML model’s cost function is not what you *really* want to optimise for, because your problem is usually a function of the ML model’s metric (e.g. optimise log loss to improve accuracy).

    Therefore the best H becomes really the best *observed* H of correlated (e.g. acc is somewhat correlated with log loss) or, worse, accidental but desirable properties of the model.

    To severely probe an ML model in the context of a *specific problem*, one needs to show that a change in the model was expected to influence the problem solution in a specific way and not others.

    People also tune hyperparameters to death via so-called ‘grid search’. This is a prototypical example in my opinion of how to *not* learn from error. For me, a severe test for hyperparam tuning is to show a plausible *path* of your hyperparam search.

    PS: Important position paper on machine learning practices: “On Pace, Progress, and Empirical Rigor” https://t.co/A2qMyMx204 https://t.co/M1XeC4ncbr

    • This conversation began with a tweet by Frank Harrell:

I welcome constructive comments that are of relevance to the post and the discussion, and discourage detours into irrelevant topics, however interesting, or unconstructive declarations that "you (or they) are just all wrong". If you want to correct or remove a comment, send me an e-mail. If readers have already replied to the comment, you may be asked to replace it to retain comprehension.

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