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m Task 18 (cosmetic): eval 12 templates: del empty params (1×); hyphenate params (3×); |
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This function is undefined when <math>p(1\mid x)=1</math> or <math>p(1\mid x)=0</math> (tending toward ∞ and −∞ respectively), but predicts a smooth curve which grows when <math>p(1\mid x)</math> increases and equals 0 when <math>p(1\mid x)= 0.5</math>.<ref name="mitlec" />
It's easy to check that the logistic loss and binary [[cross entropy]] loss (Log loss) are in fact the same (up to a multiplicative constant <math>\frac{1}{\log(2)}</math>). The cross entropy loss is closely related to the [[Kullback–Leibler divergence]] between the empirical distribution and the predicted distribution. The cross entropy loss is ubiquitous in modern [[deep learning|deep neural networks]].
== Exponential loss ==
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