Why We Don't Use the Mean Squared Error (MSE) Loss in Classification
In this video we discuss why the mean squared error (MSE) loss is not used for classification problems. We take a look at three important aspects: (1) the MSE assumes a gaussian prior, (2) the MSE applied on classification problems results in a non-convex function and (3) the MSE doesn't penalise well enough the errors in classification compared to the binary cross entropy loss function.
References
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Gaussian distribution explained: • Multivariate Normal (Gaussian) Distri...
Binary cross entropy prior for Bernoulli distribution: https://towardsdatascience.com/where-...
Demonstration that the binary cross entropy loss for classification is convex: https://towardsdatascience.com/why-no...
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Contents
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00:00 - Intro - MSE for classification
01:12 - Reason 1 - MSE assumes a gaussian prior
04:15 - Reason 2 - MSE non-convexity
08:03 - Reason 3 - MSE weak penalisation
08:42 - Outro
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