Loss functions for classification: Difference between revisions

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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''>f: X {{-->}}\mapsto y {{Y=}}{-1,1}}}</math> represents

Loss