# OK to use residual sum of squares for cross-validation of binary outcome?

For an OLS model the mean squared error can be used to assess the fit of the trained model on the validation data.

What is the equivalent for a logistic regression model? Can I simply use the following residual sum of squares function?

$RSS=\Sigma^N_{i=1}(y_i-{\hat p})^2$

• How about using classification rate? – pe-perry May 10 '16 at 16:19