In machine learning, Type 2 errors (also called false negatives) occur when a model incorrectly predicts that a specific condition or attribute does not exist when it actually does. For example, a medical diagnostic model might fail to detect the presence of a disease in a patient.

Type 2 errors can be a serious problem in machine learning applications, where the consequences of a false negative can be costly or harmful. For example, a model that fails to detect fraudulent activity in financial transactions could result in significant financial losses.

To reduce the risk of Type 2 errors in machine learning, several techniques can be employed, such as:

• Improve the sensitivity of the model:This is done by lowering the decision threshold for positive predictions. This may result in a higher true positive rate, but may also increase the number of false positives.
• Augmented training data:The training data is augmented with more examples of less popular categories. This helps the model learn the features of less popular categories more effectively, thus reducing false negatives.