In binary classification problems, the negative class is one of the two possible output classes. It represents the absence, falsehood, or negative outcome relative to the problem being addressed. The other class is known as the positive class.
Role of the Negative Class in Machine Learning
Understanding the negative class is crucial for interpreting the results of binary classification models. For instance, in medical diagnosis, the negative class might represent 'no disease,' whereas the positive class would be 'disease present.' The performance metrics of the model, such as precision and recall, are often calculated separately for each class to assess the model's effectiveness.
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