Description: Z-score normalization is a statistical technique used in machine learning to standardize the features of a dataset. This process involves removing the mean from the data and scaling to unit variance, allowing the features to have a distribution with a mean of zero and a standard deviation of one. This normalization is crucial when working with algorithms that are sensitive to the scale of the data, such as those using distances, as it ensures that all features contribute equally to the model’s outcome. Z-score normalization helps improve the convergence of optimization algorithms and prevents features with broader ranges from dominating the learning process. In various applications of data analysis and machine learning, this technique optimizes the performance of models, facilitating the implementation of applications that require real-time data processing, such as predictive analytics or automated classification tasks.
