Machine learning models can only train on floating-point values. However, many dataset features are not naturally floating-point values. Therefore, one important part of machine learning is transforming non-floating-point features to floating-point representations.
This transformation process, called [[Normalization]], converts floating-point numbers to a constrained range that improves model training.
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