# Function Domain

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A Function Domain is a set that a function is defined to accept as input.

**AKA:**Function Coverage, Function Source, Domain of a Function.**Context:**- It can be partially defined by one or more Function Parameter.
- …

**Counter-Example(s):****See:**Software Function, Independent Variable, Relation Domain, Predictive Function Coverage.

## References

### 2014

- http://en.wikipedia.org/wiki/Domain_of_a_function
- In mathematics, and more specifically in naive set theory, the
**domain of definition**(or simply the domain) of a function is the set of "input" or argument values for which the function is defined. That is, the function provides an "output" or value for each member of the domain.^{[1]}Conversely, the set of values the function takes on as output is termed the image of the function, which is sometimes also referred to as the range of the function.For instance, the domain of cosine is the set of all real numbers, while the domain of the square root consists only of numbers greater than or equal to 0 (ignoring complex numbers in both cases). When the domain of a function is a subset of the real numbers, and the function is represented in an

*xy*Cartesian coordinate system, the domain is represented on the*x*-axis.

- In mathematics, and more specifically in naive set theory, the

- ↑ Paley, Hiram; Weichsel, Paul M. (1966).
*A First Course in Abstract Algebra*. New York: Holt, Rinehart and Winston. p. 16.

### 2007

- Jerry R. Hobbs http://www.isi.edu/~hobbs/bgt-sequences.text
- QUOTE: We won't use these terms formally, but we can call s1 the domain of the function and s2 the range.
`(6) (forall (f s1 s2) (if (function0 f s1 s2)(domain s1 f)))`

`(7) (forall (f s1 s2) (if (function0 f s1 s2)(range s2 f)))`

- QUOTE: We won't use these terms formally, but we can call s1 the domain of the function and s2 the range.

### 1998

- (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
- Coverage: The proportion of a data set for which a classifier makes a prediction. If a classifier does not classify all the instances, it may be important to know its performance on the set of cases for which it is “confident
*enough to make a prediction.*

- Coverage: The proportion of a data set for which a classifier makes a prediction. If a classifier does not classify all the instances, it may be important to know its performance on the set of cases for which it is “confident