<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>

CC BY-SA 4.0
2021-10-28T20:55:43.717Z
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.
SpiderRico
CC BY-SA 4.0
2018-05-11T13:17:34.343Z
Welcome to AI!  I've taken the liberty of adding the "ai-basics" and "models" tag.
DukeZhou
<machine-learning><datasets><models><supervised-learning><explainable-ai>
What should we do when the new data drastically change the current model?
Posi2
# 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

