Scaling означает либо преобразование numerical values в standard range, либо расширение system, чтобы она могла handle larger workloads. В машинном обучении scaling часто поддерживает training stability путем normalization feature или label values; в systems design оно описывает способность поддерживать more data, users или computation.
Any mathematical transform or technique that shifts the range of a label, a feature value, or both. Some forms of scaling are very useful for transformations like normalization. Common forms of scaling useful in Machine Learning include: - linear scaling, which typically uses a combination of subtraction and division to replace the original value with a number between -1 and +1 or between 0 and 1. - logarithmic scaling, which replaces the original value with its logarithm. - Z-score normalization, which replaces the original value with a floating-point value representing the number of standard deviations from that feature's mean.