The Best Metric to Measure Accuracy of Classification Models

To understand the implication of translating the probability number, let's understand few basic concepts relating to evaluating a classification model with the help of an example given below. Since we are now comfortable with the interpretation of the Confusion Matrix, let's look at some popular metrics used for testing the classification models: Since the formula doesn't contain FP and TN, Sensitivity may give you a biased result, especially for imbalanced classes. In the example of Fraud detection, it gives you the percentage of Correctly Predicted Frauds from the pool of Actual Frauds. In the example of Fraud detection, it gives you the percentage of Correctly Predicted Frauds from the pool of Total Predicted Frauds.