There are various key metrics used for evaluation of a logistic regression model. Key metrics are as follows:

•  AUC-ROC curve - First and foremost key parameter for evaluation is AUC-ROC curve. This is the Area under Curve. The confusion matrix can be built or generated based on actual and predicted values from the model solution. Based on that, the AUC-ROC curve can be plotted to see the model PERFORMANCE. ROC stands for Receiver Operating Characteristic. For an IDEAL model, the perfect True positive rate score will be 1 and False Positive rate will be 0. The more inclined the ROC curve TOWARDS 1, the better it is.
•  AIC - Secondly, important metrics is AIC which stands for Akaike Information Criteria. This is related to the Adjusted R square value. When we look at R square and Adjusted R square, we understand that when there are more input variables being added without improving the variation explanation of target variable, then metric such as Adjusted R square penalizes if we add input variables just for the SAKE of adding and no value in terms of model performance. Hence in such cases, Adjusted R square is a better interpretation compared to R square and hence it is followed. AIC value is dependent on the Adjusted R square. Hence AIC is the goodness of fit and it penalizes if more variables are added to a model without adding value.
• Null and Residual Deviance - Null deviance and Residual deviance are other metrics which are important to evaluate a logistic regression model. Both should be low which will indicate the model is better.

Accordingly, accuracy, specificity, sensitivity parameters can be derived.

The area under the curve (AUC), referred to as an index of accuracy(A) or concordance index, is a perfect performance metric for ROC curve. Higher the area under the curve, the better is the prediction power of the model.