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False negative (FN): Difference between revisions
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False negatives can occur for various reasons, such as [[model]] complexity, imbalanced [[datasets]] and inadequate [[training data]]. Without enough training data, models that cannot capture all distributions of data will produce more false negatives; on the other hand, too complex models may lead to [[overfitting]] which also produces false negatives. | False negatives can occur for various reasons, such as [[model]] complexity, imbalanced [[datasets]] and inadequate [[training data]]. Without enough training data, models that cannot capture all distributions of data will produce more false negatives; on the other hand, too complex models may lead to [[overfitting]] which also produces false negatives. | ||
Another frequent cause of false negatives is imbalanced datasets. An imbalanced dataset occurs when one [[class]] has significantly more instances than the other, leading to models being [[Bias (ethics/fairness)|biased]] towards the majority class and producing more false negatives for minorities. | Another frequent cause of false negatives is imbalanced datasets. An imbalanced dataset occurs when one [[class]] has significantly more instances than the other, leading to models being [[Bias (ethics/fairness)|biased]] towards the [[majority class]] and producing more false negatives for minorities. | ||
==Strategies to reduce False Negatives== | ==Strategies to reduce False Negatives== | ||