Answers.Body: <p>It's possible since when two factors are correlated their combined effect on the output might not align with their individual effects due to interaction effect influencing the output in unexpected ways. Include interaction terms if your model is linear regression. Your model might not be complex enough or might have biases preventing it from learning the true relationship.</p>
  <p>Furthermore there might be other features not included in your model that <em>confound</em> the questionable relationship between temperature and the output, through their potential interaction effect with temperature's effect on the output.</p>
  <p>Finally if your features are not appropriately scaled or standardized, it can distort their relationships with the output.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-11-28T06:37:02.543Z
Answers.LastActivityDate: 2024-11-28T06:37:02.543Z
Answers.UserDisplayName: cinch
Body: <p>in my model, i have temperature and soil moisture deficit, their correlation coefficient is 0.46. and they are both negative correlated with the output. however, when I try to use sensitivity analysis, the output increase with the increase in temperature and decrease with the increase in soil moisture deficit. i dont know what happened to the temperature, is that possible because of correlation? but 0.46 is not that much high. I try to improve my model, is still cant capture the relation with temperature.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-02T10:56:31.297Z
Comments.Text: What correlation coefficient are you computing? What do you mean by "i dont know what happened to the temperature"? What is your model?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-03T11:04:56.237Z
Comments.Text: its Pearson correlation, if i use sensitivity analysis, the output should be decrease with the increase in temperature, but it is opposite in my model. the model is BR-BPNN.
Comments.UserDisplayName: Wenchuang Zhang
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-03T11:05:43.683Z
Comments.Text: Ok, please, edit your post to include this info there directly. thanks!
Comments.UserDisplayName: nbro
Tags: <machine-learning><correlation>
Title: correlation in machine learning
UserDisplayName: Wenchuang Zhang
ThreadText: # correlation in machine learning
  
  (Asked by: username_0 on 2024-11-27)
  
  in my model, i have temperature and soil moisture deficit, their correlation coefficient is 0.46. and they are both negative correlated with the output. however, when I try to use sensitivity analysis, the output increase with the increase in temperature and decrease with the increase in soil moisture deficit. i dont know what happened to the temperature, is that possible because of correlation? but 0.46 is not that much high. I try to improve my model, is still cant capture the relation with temperature.
  
  **Tags:** machine-learning, correlation
  
  > **Comments:**
  > **username_1**: What correlation coefficient are you computing? What do you mean by "i dont know what happened to the temperature"? What is your model?  
  > **username_0**: its Pearson correlation, if i use sensitivity analysis, the output should be decrease with the increase in temperature, but it is opposite in my model. the model is BR-BPNN.  
  > **username_1**: Ok, please, edit your post to include this info there directly. thanks!  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-11-28. Score: 0
  
  It's possible since when two factors are correlated their combined effect on the output might not align with their individual effects due to interaction effect influencing the output in unexpected ways. Include interaction terms if your model is linear regression. Your model might not be complex enough or might have biases preventing it from learning the true relationship.
  
  Furthermore there might be other features not included in your model that *confound* the questionable relationship between temperature and the output, through their potential interaction effect with temperature's effect on the output.
  
  Finally if your features are not appropriately scaled or standardized, it can distort their relationships with the output.
  
  
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