Normalization

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Normalization is a preprocessing step that adjusts numeric features so they share a similar scale, often by mapping values into a range like 0 to 1 or by standardizing them around a mean. This matters because many AI models react strongly to differences in feature size. For example, if one feature ranges in the thousands while another is between 0 and 1, the larger one can dominate learning unless the values are balanced. By normalizing, each feature contributes more evenly, helping the model learn patterns instead of being influenced by raw magnitude.

Common normalization methods include min–max scaling, which maps values to a fixed interval, and other feature-scaling techniques that reduce large numeric differences. Normalization doesn’t change what the data means, it simply puts all features on comparable scales so the model can learn more effectively.

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