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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?

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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.

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  • $\begingroup$ But then what does it try to minimize if not the distance between f(x)-y $\endgroup$
    – Jacky02
    Jul 1, 2023 at 20:17
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    – Community Bot
    Jul 3, 2023 at 15:26

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