Loss functions for classification

This sandbox is in the article namespace. Either move this page into your userspace, or remove the {{User sandbox}} template. Loss function surrogates for classification are computationally feasible loss functions representing the price we will pay for inaccuracy in our predictions in classification problems. ^[1] Specifically, if ${\displaystyle g:X\mapsto }$ {-1,1} represents the mapping of a vector ${\displaystyle {\vec {x}}\in X}$ to a class label ${\displaystyle y\in }${-1,1}, we wish to find a function ${\displaystyle f:X\mapsto \mathbb {R} }$ which best approximates the true mapping ${\displaystyle g}$. (citation needed)

Square Loss

Hinge Loss

Main article: Hinge loss

Logistic Loss

References

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