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Very balanced dataset and a multiclass classification problem, no context behind the inputs. Which evaluation metric to use?
I have constructed a simple neural network model, for a classification problem, with 10 target classes where an input (with some number of features) is to be classified to only one of the 10 classes.
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Categorical cross-entropy vs Binary cross-entropy for multi-class classification with mixup
I understand that for multi-class classification the correct loss to use is categorical cross-entropy. However, when performing mixup as a regularisation technique two samples $(X_1, y_1)$ and $(X_2, ...
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