scaling

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.

Practitioners refer to scaling when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.