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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