Feature scaling is the process of normalizing or standardizing the range of independent variables or features in data. This practice is crucial because it ensures that all features contribute equally to the distance calculations, which can be particularly important in algorithms that compute distances, like clustering and certain machine learning models. By adjusting the scale of features, it helps improve model performance and training stability.
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