False Positive Error Rate

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A False Positive Error Rate is a binary classification performance measure that is based on the Probability that a Predictive Relation will Incorrectly Predict that a False Test Instance is a True Test Instance (i.e. make a Positive Prediction).




  • (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Type_I_and_type_II_errors#Type_I_error
    • QUOTE: Type I error, also known as an "error of the first kind", an [math]\displaystyle{ α }[/math] error, or a "false positive": the error of rejecting a null hypothesis when it is actually true. Plainly speaking, it occurs when we are observing a difference when in truth there is none. An example of this would be if a test shows that a woman is pregnant when in reality she is not. Type I error can be viewed as the error of excessive credulity.