Why you should care about debugging machine learning models
O'Reilly on Data
DECEMBER 12, 2019
In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. 8] Data about individuals can be decoded from ML models long after they’ve trained on that data (through what’s known as inversion or extraction attacks, for example).
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