Min-max scaling is a normalization technique used to transform features to a fixed range, typically [0, 1]. This process ensures that each feature contributes equally to the distance calculations in algorithms, making it essential for data preparation in predictive modeling. By adjusting the values of a feature based on its minimum and maximum values, this method helps mitigate the influence of outliers and different measurement scales across features.
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