Признак — это входная переменная или измеримая характеристика, используемая моделью машинного обучения для представления наблюдения. Признаки могут быть сырыми атрибутами, производными значениями или извлеченными сигналами из данных, таких как текст, изображения, аудио или табличные записи.
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.