Fallout Precision
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A Fallout Precision is an Information Retrieval Evaluation Measure that is used to determine the proportion of non-relevant documents retrieved by the query.
- AKA: Fallout, Fallout Evaluation Measure.
- …
- Example(s):
number of incorrect answers given by the system / number of spurious facts in the text- [math]\displaystyle{ \mbox{fall-out}=\frac{|\{\mbox{irrelevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{irrelevant documents}\}|} }[/math]
- Counter-Example(s)
- See: Controlled Indexing Task, Information Extraction, Offline Metric, Recevier Operating Characteristic Curve.
References
2018a
- (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Fall-out Retrieved:2018-2-16.
- The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available: : [math]\displaystyle{ \mbox{fall-out}=\frac{|\{\mbox{non-relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{non-relevant documents}\}|} }[/math] In binary classification, fall-out is closely related to specificity and is equal to [math]\displaystyle{ (1-\mbox{specificity}) }[/math] . It can be looked at as the probability that a non-relevant document is retrieved by the query.
It is trivial to achieve fall-out of 0% by returning zero documents in response to any query.
- The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available: : [math]\displaystyle{ \mbox{fall-out}=\frac{|\{\mbox{non-relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{non-relevant documents}\}|} }[/math] In binary classification, fall-out is closely related to specificity and is equal to [math]\displaystyle{ (1-\mbox{specificity}) }[/math] . It can be looked at as the probability that a non-relevant document is retrieved by the query.
2018b
- (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Information_retrieval#Fall-out Retrieved:2018-2-17.
- The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available: : [math]\displaystyle{ \mbox{fall-out}=\frac{|\{\mbox{non-relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{non-relevant documents}\}|} }[/math] In binary classification, fall-out is closely related to specificity and is equal to [math]\displaystyle{ (1-\mbox{specificity}) }[/math] . It can be looked at as the probability that a non-relevant document is retrieved by the query.
It is trivial to achieve fall-out of 0% by returning zero documents in response to any query.
- The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available: : [math]\displaystyle{ \mbox{fall-out}=\frac{|\{\mbox{non-relevant documents}\}\cap\{\mbox{retrieved documents}\}|}{|\{\mbox{non-relevant documents}\}|} }[/math] In binary classification, fall-out is closely related to specificity and is equal to [math]\displaystyle{ (1-\mbox{specificity}) }[/math] . It can be looked at as the probability that a non-relevant document is retrieved by the query.
2008
- (Egghe, 2008) ⇒ Egghe, L. (2008). "The measures precision, recall, fallout and miss as a function of the number of retrieved documents and their mutual interrelations". Information Processing & Management, 44(2), 856-876.
- ABSTRACT: In this paper, for the first time, we present global curves for the measures precision, recall, fallout and miss in function of the number of retrieved documents. Different curves apply for different retrieved systems, for which we give exact definitions in terms of a retrieval density function: perverse retrieval, perfect retrieval, random retrieval, normal retrieval, hereby extending results of Buckland and Gey and of Egghe in the following sense: mathematically more advanced methods yield a better insight into these curves, more types of retrieval are considered and, very importantly, the theory is developed for the complete set of measures: precision, recall, fallout and miss. Next we study the interrelationships between precision, recall, fallout and miss in these different types of retrieval, hereby again extending results of Buckland and Gey (incl. a correction) and of Egghe. In the case of normal retrieval we prove that precision in function of recall and recall in function of miss is a concavely decreasing relationship while recall in function of fallout is a concavely increasing relationship. We also show, by producing examples, that the relationships between fallout and precision, miss and precision and miss and fallout are not always convex or concave.
2001
- (Jacquemin, 2001) ⇒ Christian Jacquemin. (2001). “Spotting and Discovering Terms Through Natural Language Processing." MIT Press. ISBN:0262100851
- 'Precision of fallout: The precision of fallout in controlled indexing is the proportion of correct indexes among the rejected occurrences.
2000
- 2000_SpeechAndLanguageProcessing.
- Fallout is a measure of the systems ability to ignore spurious information in the text.