False negative (FN): Revision history

17 March 2023

• curprev 21:0021:00, 17 March 2023‎ Alpha5 talk contribs‎ m 3,559 bytes +25‎ Text replacement - "Category:Machine learning terms" to "Category:Machine learning terms Category:not updated"

20 February 2023

• curprev 14:4314:43, 20 February 2023‎ Alpha5 talk contribs‎ 3,534 bytes +4‎ No edit summary
• curprev 13:1413:14, 20 February 2023‎ Alpha5 talk contribs‎ m 3,530 bytes 0‎ Text replacement - "classes" to "classes"
• curprev 00:4800:48, 20 February 2023‎ Alpha5 talk contribs‎ 3,530 bytes −1‎ No edit summary
• curprev 00:4700:47, 20 February 2023‎ Alpha5 talk contribs‎ 3,531 bytes +37‎ No edit summary
• curprev 00:4700:47, 20 February 2023‎ Alpha5 talk contribs‎ 3,494 bytes +858‎ No edit summary
• curprev 00:3600:36, 20 February 2023‎ Alpha5 talk contribs‎ 2,636 bytes −338‎ No edit summary

19 February 2023

• curprev 23:3823:38, 19 February 2023‎ Alpha5 talk contribs‎ 2,974 bytes −365‎ No edit summary
• curprev 17:1917:19, 19 February 2023‎ Alpha5 talk contribs‎ 3,339 bytes +3,339‎ 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..."