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'''Loss function surrogates for classification''' are computationally feasible [[loss functions]] representing the price we will pay for inaccuracy in our predictions in classification problems. <ref>{{cite doi|10.1162/089976604773135104}}</ref> Specifically, if <math>g: X \mapsto</math> {-1,1} represents the mapping of a vector <math>\vec{x} \in X</math> to a class label <math>y \in </math> {-1,1}, we wish to find a function <math>f: X \mapsto \mathbb{R}</math> which best approximates the true mapping <math>g</math>. (citation needed) Given that loss functions are always true functions of only one variable, it is standard practice to define loss functions in terms of the product of the true classifier <math>y</math> and the predicted value <math>f(\vec{x})</math>.
== Square Loss ==
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