False negative (FN): Difference between revisions

In [[binary classification]], a '''false negative''' can be defined as when the model incorrectly classifies an input into the negative class when it should have been classified as positive. For instance, in medical diagnosis tasks, false negatives may occur when models predict that patients do not have diseases when they actually do have them. Such false negatives have serious repercussions as patients may not receive appropriate treatments due to misclassified data.

To evaluate the performance of a machine learning model, various metrics are employed. Recall and sensitivity are two commonly used metrics to measure false negatives.

Recall, also known as true positive rate (TPR), is defined as the ratio of correctly identified positive instances to all actual positive instances. In other words, recall measures the percentage of correctly classified positive instances according to a model's predictions. A low recall value suggests that the model may fail to recognize many positives, leading to more false negatives than usual.

 

Sensitivity, also referred to as hit rate or true positive rate, is similar to recall and measures the proportion of actual positives correctly identified by a model. Sensitivity can be calculated as the ratio of true positives over all true positives plus false negatives. A high sensitivity value suggests that the model is correctly identifying most of these instances.

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 biased towards the majority class and producing more false negatives for minorities.