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In machine learning (in particular, supervised learning), if some new data changes the previous model/function drastically, then I think we should study that data. Does it happen? How to handle such a situation?

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    $\begingroup$ Welcome to AI! I've taken the liberty of adding the "ai-basics" and "models" tag. $\endgroup$
    – DukeZhou
    May 11 '18 at 13:17
  • $\begingroup$ I'd guess the new data has a different distribution then what the model has seen up to that point. So, distribution shift would be the first thing that'd come to my mind. $\endgroup$
    – SpiderRico
    Oct 28 '21 at 20:55

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