Sensitivity
Sensitivity is a performance metric that measures how effectively a model identifies positive cases. It represents the proportion of actual positive cases that are correctly detected by the model.
What is Sensitivity?
Sensitivity, also called True Positive Rate (TPR) or Recall, measures how many actual positive cases a model correctly identifies. It is calculated as TP / (TP + FN), where TP represents true positives and FN represents false negatives. Higher sensitivity means fewer positive cases are missed.
Why is Sensitivity Important?
Sensitivity is especially important when missing a positive case has significant consequences. It helps evaluate whether a model can reliably detect threats, diseases, fraud, or other events that need to be identified. However, increasing sensitivity can sometimes result in more false positives.
Common use cases
Sensitivity is commonly used in medical diagnosis, fraud detection, security threat detection, classification models, and other applications where identifying as many true positive cases as possible is important.