Нормализация

Normalization is the process of scaling floating point data to values between 0 and 1. Many of the training algorithms used in ML.NET require input feature data to be normalized. ML.NET provides a series of transforms for normalization .

Broadly speaking, the process of converting a variable's actual range of values into a standard range of values, such as: - -1 to +1 - 0 to 1 - Z-scores (roughly, -3 to +3) For example, suppose the actual range of values of a certain feature is 800 to 2,400. As part of feature engineering, you could normalize the actual values down to a standard range, such as -1 to +1. Normalization is a common task in feature engineering. Models usually train faster (and produce better predictions) when every numerical feature in the feature vector has roughly the same range. See also Z-score normalization. See Numerical Data: Normalization in Machine Learning Crash Course for more information.