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by Dstillery Contributor


Chief Scientist, Claudia Perlich, joined Data Skeptic’s podcast to talk 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. Their discussion covers some novel observations about ROC and AUC, as well as an informative discussion of leakage.