False negative (FN)

Introduction

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.

How to measure False Negatives?

To evaluate the performance of a machine learning model, various metrics are employed. Recall is a commonly used metric 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.

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

Strategies to reduce False Negatives

There are several strategies to reduce false negatives in machine learning models. One of the most efficient solutions is using more training data, which helps the model capture all distributions of data. Furthermore, data augmentation techniques like oversampling minority classes or undersampling majority classes can help balance out the dataset.

One strategy is to use a different evaluation metric such as precision or F1 score, which accounts for both false positives and false negatives. Precision measures the percentage of true positive predictions out of all positive predictions, while F1 score is an average of precision and recall.

Finally, selecting an appropriate model for the problem at hand is critical. A model that's too simplistic may not be able to fully capture all of the complexity in your data.