0-1 Loss Function explanation

You have correctly summarized the 0-1 loss function as effectively looking at accuracy. Your 1's become indicators for misclassified items, regardless of how they were misclassified. Since you have three 1's out of 10 items, your classification accuracy is 70%. If you change the weighting on the loss function, this interpretation doesn't apply anymore. For example, in disease classification, it might be more costly to miss a positive case of disease (false negative) than to falsely diagnose disease (false positive).