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.

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