Commonly we can say that accuracy is defined as total positive/ total nbr cases.
But I read that, when it is a binary classifier we should consider: TP+TN/ total nbr cases.
Can someone explain to me why? Thanks
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Accuracy is commonly defined as total nbr of correct predictions / total nbr of predictions. Imagine a binary classification of color in: White or not White.
Since it's binary, all the white Classifications - a True Positive classification are as important for accuracy as a - True Negative classification, after all, the classification is also correct, hence, TP+TN/ total nbr of predictions.