What is negative class?
The class or category in a classification problem that represents the absence or opposite of the desired outcome
negative class explained in plain English
In machine learning, a negative class refers to the group of data points that do not exhibit the characteristic or behavior of interest. For example, in a medical diagnosis model, the negative class might represent patients who do not have a particular disease.
Analogy
Think of a negative class like a 'no' or 'absent' category. Just as a 'yes' or 'present' category would represent the presence of something, a negative class represents the absence of it.
Example
In a spam detection model, the negative class would represent emails that are not spam, while the positive class would represent emails that are spam.
How is negative class used?
Negative classes are used in supervised learning to train models to distinguish between the presence and absence of a particular characteristic or behavior. The model learns to predict the probability of an instance belonging to the negative class or the positive class.
Common misconceptions about negative class
A common misconception is that the negative class is always the 'bad' or 'undesirable' category. However, this is not always the case. The negative class simply represents the absence of the characteristic or behavior of interest.
History
The concept of a negative class has been around since the early days of machine learning and has been used in various forms of classification problems.
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