A feature is an input variable or measurable characteristic used by a machine learning model to represent an observation. Features may be raw attributes, derived values, or extracted signals from data such as text, images, audio, or tabular records.
A measurable property of the phenomenon being measured, typically a numeric (double) value. Multiple features are referred to as a Feature vector and typically stored as double[] . Features define the important characteristics of the phenomenon being measured. For more information, see the Feature article on Wikipedia.
An individual measurable property or characteristic of a phenomenon. In machine learning, a feature is often an input variable or attribute used by a model to represent an observation for tasks such as prediction, classification, and pattern recognition. In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in an image (such as points, edges, or objects), or the result of a general neighborhood operation or feature detection app
An input variable to a machine learning model. An example consists of one or more features. For instance, suppose you are training a model to determine the influence of weather conditions on student test scores. The following table shows three examples, each of which contains three features and one label: Label | Humidity Test score | ------| 47 92 | 34 84 | 92 87 | Contrast with label. See Supervised Learning in the Introduction to Machine Learning course for more information.