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?


1 Answer 1


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:



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