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Feature Scaling – Normalization Vs Standardization Explained in Simple Terms – Machine Learning Basics

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Feature scaling is a preprocessing technique used in machine learning to standardize or normalize the range of independent variables (features) in a dataset. The primary goal of feature scaling is to ensure that no particular feature dominates the others due to differences in the units or scales. By transforming the features to a common scale, it helps improve the performance, stability, and convergence speed of machine learning algorithms.

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