Metrics for Evaluating Performance of Logistic Regression I built a Logistic Regression model and I would like to evaluate the performance of the model. I would like to understand its evaluation metrics.

What do the metrics Sensitivity, Specificity, False Positives Rate, Precision, Recall, and Accuracy tell us about this model?
Since Logistic regression is not same as Linear regression , predicting just accuracy will mislead. 
** Confusion Matrix** is one way to evaluate the performance of your model. Checking the values of True Positives, False Negatives ( Type II Error) are really important.

** ROC Curve** Receiver Operating Characteristic(ROC) summarizes the model’s performance by evaluating the trade offs between true positive rate (sensitivity) and false positive rate(1- specificity)

The below link will give you more information:

https://www.analyticsvidhya.com/blog/2015/11/beginners-guide-on-logistic-regression-in-r/