Type II Error (False Negative)
- Definition: A Type II error occurs when the model incorrectly predicts the negative class. This means it fails to identify a positive instance.
- Example: If a model predicts that an email is not spam (negative) when it is actually spam (positive), this is a Type II error.
- Consequences: Type II errors can lead to missed opportunities or risks, such as allowing spam emails to clutter the inbox or failing to detect a disease in a medical diagnosis scenario.