# Predictor Feature Function

(Redirected from Regressor)

A Predictor Feature Function is a function structure that represents a independent random variable (i.e. intended to provide a signal to a predictive model).

## References

### 2015a

1. Bishop, Christopher (2006). Pattern recognition and machine learning. Berlin: Springer. ISBN 0-387-31073-8.

### 2015b

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Dependent_and_independent_variables#Statistics_synonyms Retrieved:2015-6-6.
• An independent variable is also known as a "predictor variable", "regressor", "controlled variable", "manipulated variable", "explanatory variable", “exposure variable” (see reliability theory), “risk factor” (see medical statistics), “feature” (in machine learning and pattern recognition) or an "input variable."[1] [2] "Explanatory variable"is preferred by some authors over "independent variable" when the quantities treated as "independent variables" may not be statistically independent.[3] [4]

A dependent variable is also known as a "response variable", "regressand", "measured variable", "responding variable", "explained variable", "outcome variable", "experimental variable", and "output variable".[2] If the independent variable is referred to as an "explanatory variable" (see above) then the term "response variable"is preferred by some authors for the dependent variable.[2][3][4]

Variables may also be referred to by their form: continuous, binary/dichotomous, nominal categorical, and ordinal categorical, among others.

1. Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9 (entry for "independent variable")
2. Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9 (entry for "regression")
3. Everitt, B.S. (2002) Cambridge Dictionary of Statistics, CUP. ISBN 0-521-81099-X
4. Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9

### 2012

• (Wikipedia, 2012) ⇒ http://en.wikipedia.org/wiki/Covariate
• QUOTE: In statistics, a covariate is a variable that is possibly predictive of the outcome under study. A covariate may be of direct interest or it may be a confounding or interacting variable.

The alternative terms explanatory variable, independent variable, or predictor, are used in a regression analysis. In econometrics, the term "control variable" is usually used instead of "covariate". In a more specific usage, a covariate is a secondary variable that can affect the relationship between the dependent variable and other independent variables of primary interest.

An example is provided by the analysis of trend in sea-level by Woodworth (1987). Here the dependent variable (and variable of most interest) was the annual mean sea level at a given location for which a series of yearly values were available. The primary independent variable was "time". Use was made of a "covariate" consisting of yearly values of annual mean atmospheric pressure at sea level. The results showed that inclusion of the covariate allowed improved estimates of the trend against time to be obtained, compared to analyses which omitted the covariate.

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### 2008

• (Wilson, 2008a) ⇒ Bill Wilson. (2008). “The Machine Learning Dictionary for COMP9414." University of New South Wales, Australia.
• QUOTE: attributes: An attribute is a property of an instance that may be used to determine its classification. For example, when classifying objects into different types in a robotic vision task, the size and shape of an instance may be appropriate attributes. Determining useful attributes that can be reasonably calculated may be a difficult job - for example, what attributes of an arbitrary chess end-game position would you use to decide who can win the game? This particular attribute selection problem has been solved, but with considerable effort and difficulty. Attributes are sometimes also called features.

### 2000

• http://www.cse.unsw.edu.au/~billw/mldict.html#attribute
• QUOTE: An attribute is a property of an instance that may be used to determine its classification. For example, when classifying objects into different types in a robotic vision task, the size and shape of an instance may be appropriate attributes. Determining useful attributes that can be reasonably calculated may be a difficult job - for example, what attributes of an arbitrary chess end-game position would you use to decide who can win the game? This particular attribute selection problem has been solved, but with considerable effort and difficulty.

Attributes are sometimes also called features.