Negative Class
Negative Class refers to the category representing the absence of the condition, event, or outcome that a machine learning model is designed to detect. It is commonly used in binary classification alongside the positive class.
What is Negative Class?
In a binary classification problem, each example is assigned to either a positive or negative class. For example, in spam detection, spam may be the positive class and legitimate messages the negative class. The meaning of the negative class depends on the specific task and how the classification labels are defined.
Why is Negative Class Important?
Clearly defining the negative class is important for interpreting model predictions and evaluation metrics such as false positives, false negative rate, specificity, and precision. It also helps ensure that training and evaluation datasets use consistent labels.
Common use cases
Negative classes are commonly used in spam detection, fraud detection, medical diagnosis, content moderation, anomaly detection, and other binary classification tasks.