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For the term 'predictor', I found the following definition:

Predictor Variable: One or more variables that are used to determine or predict the target variable.

Whereas Wikipedia contains the following definition of the word 'feature':

Feature is an individual measurable property or characteristic of a phenomenon being observed.

What is the difference between 'predictor' and 'feature' in machine learning?

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  • $\begingroup$ No difference for me... Matlab, for instance, uses the name predictor for feature $\endgroup$ – ignatius Dec 20 '18 at 17:06
  • $\begingroup$ The difference is likely in the community that uses the terms. For computer vision/ml, the term "feature" is commonly used. $\endgroup$ – Martin Thoma Dec 20 '18 at 17:22
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Feature and predictor are used interchangeably in machine learning today though I must admit that it seems that feature is being used more than predictor. The definition is the one on Wikipedia which you have already mentioned. The term predictor comes from statistics, here one definition:

An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable.

and my favorite definition: A predictor variable explains changes in the response.

In a nutshell: X columns: features, predictors, independent variables, experimental variables. y column(s): target, dependent variable, outcome, outcome variable.

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