Answer Posted / Priyanka Kumari

Type I Error (False Positive) occurs when a classification model incorrectly identifies an instance as belonging to a certain class, while Type II Error (False Negative) happens when it fails to identify an instance that truly belongs to the given class. In other words, Type I Error is a mistake of rejecting a true positive, and Type II Error is a mistake of accepting a false negative.

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