# FAQ: Logistic Regression - Log-Loss I

This community-built FAQ covers the “Log-Loss I” exercise from the lesson “Logistic Regression”.

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## FAQs on the exercise Log-Loss I

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I’m using the interactive visualiser to create a sigmoid curve and I was wondering why the ‘Best Logistic Regression Curve’ doesn’t mean the smallest log-loss? As in, sometimes I can choose different b_0, b_1 that produce a smaller log-loss value.

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Systemwhiz, I was wondering the same thing. After plotting random points I can consistently adjust b_1 and b_0 to create a Log_Loss lower than that created by checking the box next to ‘Plot Best Logistic Regression Curve.’ This seems to contradict the instructional text stating, "The goal of our Logistic Regression model is to find the feature coefficients and intercept…that minimize log-loss for our training data!

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Same question here. In fact, the automatic plotter always shows more like linearlike curves, than clearly sigmoidal. if you reduce a lot the b_1 it is easy to get almost no loss. what is the point here?