Sensitivity: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Sensitivity = '''Sensitivity''', also known as '''Recall''' or the '''True Positive Rate (TPR)''', is a performance metric used in classification problems. It measures how well a model can identify actual positive instances. == Definition == :<math> \text{Sensitivity} = \frac{TP}{TP + FN} </math> Where: * '''TP''' = True Positives – actual positives correctly predicted * '''FN''' = False Negatives – actual positives incorrectly predicted as negative Sensitivit..." |
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sensitivity in machine learning, true positive rate, recall vs sensitivity, disease test sensitivity, model evaluation metric, sensitivity formula, confusion matrix sensitivity | sensitivity in machine learning, true positive rate, recall vs sensitivity, disease test sensitivity, model evaluation metric, sensitivity formula, confusion matrix sensitivity | ||
[[Category:Artificial Intelligence]] | |||