Types of Errors

Last updated Sep 25, 2022 Edit Source

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In classification, an error is often represented by the symbol $\epsilon$. A misclassification can be one of two types:

• False Positive (Type I): A false positive is misclassfying a given data point to belong to a class while it actually is not.
• False Negative(Type II): A false negative is misclassfying a data point to not belong to a class while in fact it does.