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False negative (FN): Difference between revisions
Created page with "==Introduction== In machine learning, a false negative (FN) occurs when a model predicts a negative outcome for an input when the true outcome is positive. In other words, this occurs when the model fails to identify positive instances correctly. False negatives are frequently linked with Type II errors in statistics - when one fails to reject a null hypothesis when it is actually false. In binary classification, a false negative can be defined as when the model incorre..."
(Created page with "==Introduction== In machine learning, a false negative (FN) occurs when a model predicts a negative outcome for an input when the true outcome is positive. In other words, this occurs when the model fails to identify positive instances correctly. False negatives are frequently linked with Type II errors in statistics - when one fails to reject a null hypothesis when it is actually false. In binary classification, a false negative can be defined as when the model incorre...") |
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