Feature Scaling (Normalization)

Category: science

The process of scaling numerical features to a standard range (usually 0 to 1).

AI models get confused if one input is "Age" (0–100) and another is "Income" (0–1,000,000). The model will assume Income is way more important just because the number is bigger. Normalization forces every feature to the same scale so the model weights them fairly.

Common Examples

• We applied feature scaling to the income data to ensure the model didn’t inappropriately bias the risk predictions toward higher earners.
• Without proper normalization, your model’s weightings will be distorted by the varying units of measure in your input datasets.