Natural Language Processing MCQ - Impact of FALSE POSITIVE on precision

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Natural Language Processing MCQ - Impact of FALSE POSITIVE on the system's precision mesure

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1. A classifier that makes a lot of FALSE POSITIVEs will have ________.

a) Low recall

b) Low precision

c) High recall

d) High precision

 

Answer: (b) Low precision What is False Positive (FP)? False positive is when the system (classifier) incorrectly classifies a wrong event as correct (positive) class. In other words, how many negative cases get incorrectly identified as positive. It is also referred as TYPE I error. Let us assume that we have developed a house alarm security system to automatically detect the presence of intruders. FALSE POSITIVE is, your alarm goes off when there is no intruder. Few other examples; A diagnosis system detects that a patient is affected with a disease which he actually not. A fire alarm system alarms when actually there is no fire. A financial system detects suspicious transactions which are actually genuine.   PRECISION is calculated as follows; Precision = TP / (TP + FP) If the system detects more FPs, it lowers the PRECISION of the underlying system. Hence, while building classification models, you may have to choose to build models with lower false positives if a high precision score is desirable as per your requirements.

 

 

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