June 11, 2024, 2:09 p.m. | Shaique Hossain

DEV Community dev.to

Data normalization in machine learning involves transforming numerical features to a standard scale to ensure fair contribution to model training. Techniques like Min-Max Scaling and Z-score Standardization are commonly used. Normalization enhances model convergence, prevents dominant features, and improves algorithm performance, especially in distance-based and gradient-descent algorithms. However, its necessity depends on the algorithm and dataset characteristics.

algorithm algorithms convergence data data normalization datanormalization datascience fair features gradient gradient-descent however linearregression machine machine learning machinelearning machine learning data max min normalization numerical performance scale scaling standard standardization the algorithm training z-score

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