50 min listen
Predictive Models on Random Data
FromData Skeptic
ratings:
Length:
37 minutes
Released:
Jul 22, 2016
Format:
Podcast episode
Description
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!
Released:
Jul 22, 2016
Format:
Podcast episode
Titles in the series (100)
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