Specificity: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Specificity = '''Specificity''', also known as the '''True Negative Rate (TNR)''', is a performance metric in binary classification tasks. It measures the proportion of actual negative instances that are correctly identified by the model. == Definition == :<math> \text{Specificity} = \frac{TN}{TN + FP} </math> Where: * '''TN''' = True Negatives – actual negatives correctly predicted * '''FP''' = False Positives – actual negatives incorrectly predicted as positi..." |
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== Specificity vs Sensitivity == | == Specificity vs Sensitivity == | ||
These are | These are [[complementary metrics]]: | ||
* '''Sensitivity''' = Ability to detect positives | * '''Sensitivity''' = Ability to detect positives | ||
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specificity in machine learning, true negative rate, sensitivity vs specificity, specificity formula, confusion matrix specificity, model evaluation metrics, binary classification | specificity in machine learning, true negative rate, sensitivity vs specificity, specificity formula, confusion matrix specificity, model evaluation metrics, binary classification | ||
[[Category:Artificial Intelligence]] | |||