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y_class = np.argmax(y_pred, axis = 1) here axis=1 represents check max value index column wise you are facing the error because you are passing vector (size,) y_class = np.argmax(y_pred, axis = 0) use above code to remove the error


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If your classes have a natural order, like exam grades, you can encode then as consecutive integers. That way you can plot a confusion matrix and also give the mean absolute error in your predictions when they are treated as integer values. Watch out for unequal distances between adjacent classes, which might make a metric like MAE misleading depending on ...


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The classes that define the columns/rows can be arbitrarily rearranged. Therefore, the "distance" of a misclassification to the diagonal has no meaning. So no, there is no such metric. I like @Dave's comment: "Is it worse to call a dog a cat than it is to call a dog a horse?" Maybe you'd ask yourself, "some classes feel closer ...


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