Answers.Body: <p>I believe you are correct. In linear regression you are predicting a continuous numerical value but in logistic regression you are trying to perform classification tasks so you want to minimize the error but you also don't want to over fit to the data as well.</p>
  
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Answers.Text: But then what does it try to minimize if not the distance between f(x)-y
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Body: <p>In linear regression, I train the model so the graph runs best through the data points, so the geometric distance between f(x) and <span class="math-container">$y^i$</span> is minimized.</p>
  <p>Now, is it correct that in logistic regression I do not try to fit the graph perfectly through the data but instead try to mimimize the error in the predicted probabilities? Why is that a difference? And what happens when I plot the resulting graph to the dataset. Does it still look like it's perfectly fitted through the data, and why?</p>
  
Tags: <objective-functions><linear-regression><statistical-ai><logistic-regression>
Title: In logistic regression, do I try to fit the graph perfectly or mimimize the error in the predicted probabilities?
UserDisplayName: Jacky02
ThreadText: # In logistic regression, do I try to fit the graph perfectly or mimimize the error in the predicted probabilities?
  
  (Asked by: username_0 on 2023-06-30)
  
  In linear regression, I train the model so the graph runs best through the data points, so the geometric distance between f(x) and $y^i$ is minimized.
  
  Now, is it correct that in logistic regression I do not try to fit the graph perfectly through the data but instead try to mimimize the error in the predicted probabilities? Why is that a difference? And what happens when I plot the resulting graph to the dataset. Does it still look like it's perfectly fitted through the data, and why?
  
  **Tags:** objective-functions, linear-regression, statistical-ai, logistic-regression
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-07-01. Score: 0
  
  I believe you are correct. In linear regression you are predicting a continuous numerical value but in logistic regression you are trying to perform classification tasks so you want to minimize the error but you also don't want to over fit to the data as well.
  
  > **Comments:**
  > **username_0**: But then what does it try to minimize if not the distance between f(x)-y  
  > **username_2**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  
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