Z-score Normalization

A scaling technique that replaces a raw feature value with a floating-point value representing the number of standard deviations from that feature's mean. For example, consider a feature whose mean is 800 and whose standard deviation is 100. The following table shows how Z-score normalization would map the raw value to its Z-score: Z-score | ---| 0 | +1.5 | -2.25 | The machine learning model then trains on the Z-scores for that feature instead of on the raw values. See Numerical data: Normalization in Machine Learning Crash Course for more information.