What is a 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.
Z-score normalization explained in plain English
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:
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.
Example
Practitioners refer to z-score normalization when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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