Afspillet
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Kyle shares a few thoughts on mistakes observed by job applicants and also shares a few procedural insights listeners at early stages in their careers might find value in.
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This week is an insightful discussion with Claudia Perlich about some situations in machine learning where models can be built, perhaps by well-intentioned practitioners, to appear to be highly predictive despite being trained on random data. Our discussion covers some novel observations about ROC and AUC, as well as an informative discussion of leakage.
Much of our discussion is inspired by two excellent papers Claudia authored: Leakage in Data Mining: Formulation, Detection, and Avoidance and On Cross Validation and Stacking: Building Seemingly Predictive Models on Random Data. Both are highly recommended reading!
