Body: <p>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?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-10-28T20:55:43.717Z
Comments.Text: 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.
Comments.UserDisplayName: SpiderRico
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-05-11T13:17:34.343Z
Comments.Text: Welcome to AI!  I've taken the liberty of adding the "ai-basics" and "models" tag.
Comments.UserDisplayName: DukeZhou
Tags: <machine-learning><datasets><models><supervised-learning><explainable-ai>
Title: What should we do when the new data drastically change the current model?
UserDisplayName: Posi2
ThreadText: # What should we do when the new data drastically change the current model?
  
  (Asked by: username_0 on 2018-05-11)
  
  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?
  
  **Tags:** machine-learning, datasets, models, supervised-learning, explainable-ai
  
  > **Comments:**
  > **username_2**: Welcome to AI!  I've taken the liberty of adding the "ai-basics" and "models" tag.  
  > **username_1**: 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.  
  
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  ## 0 answers
  
  