# An Example Where Square Loss of a Sigmoid Prediction is not Convex in the Parameters

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I’ve added a worked `R`

example of the non-convexity, with respect to model parameters, of square loss of a sigmoid-derived prediction here.

This is finishing an example for our `Python`

note “Why not Square Error for Classification?”. Reading that note will give a usable context and background for this diagram.

The undesirable property is: such a graph says that a parameter value of `b = -1`

and `b = -0.25`

have similar losses, but parameters values in-between are worse. This might seem paradoxical, but it is an artifiact of the loss-function – not an actual property of the data or model. The same note shows the deviance loss has the desirable convex property: interpolations of good parameter values are also good.

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