Error Type I and Error Type II, also known as false positives and false negatives, are terms commonly used in the context of binary classification problems. Let’s break down these concepts:
I. Error Type I (False Positive):
1. Definition:
- Error Type I occurs when the model predicts the positive class, but the true class is negative.
2. Example:
- Scenario: A medical test for a rare disease.
- False Positive: The test incorrectly indicates the presence of the disease when the patient is actually healthy.
3. Preferable in Certain Scenarios:
- In situations where the cost or consequences of a false positive are relatively low compared to a false negative.
- When the goal is to be cautious and minimize the risk of missing positive instances.
II. Error Type II (False Negative):
1. Definition:
- Error Type II occurs when the model predicts the negative class, but the true class is positive.
2. Example:
- Scenario: Airport security screening…
