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 g: X {{-->}} y ={-1,1} represents
Loss
Square Loss
Hinge Loss
Logistic Loss
References
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