ML Metrics: Sensitivity vs. Specificity

In this post, we will try and understand the concepts behind evaluation metrics such as sensitivity and specificity, which is used to determine the performance of the Machine Learning models. The post also describes the differences between sensitivity and specificity. The concepts have been explained using the model for predicting whether a person is suffering from a disease or not.

What Is Sensitivity

Sensitivity is a measure of the proportion of actual positive cases that got predicted as positive (or true positive). Sensitivity is also termed as Recall. This implies that there will be another proportion of actual positive cases, which would get predicted incorrectly as negative (and, thus, could also be termed as the false negative). This can also be represented in the form of a false negative rate. The sum of sensitivity and false negative rate would be 1. Let’s try and understand this with the model used for predicting whether a person is suffering from the disease. Sensitivity is a measure of the proportion of people suffering from the disease who got predicted correctly as the ones suffering from the disease. In other words, the person who is unhealthy actually got predicted as unhealthy.

Source de l’article sur DZONE (AI)