Description: Z-score normalization, also known as standardization, is a data preprocessing technique that transforms the features of a dataset to have a mean of zero and a standard deviation of one. This process is done by subtracting the mean from each value and dividing the result by the standard deviation. Z-score normalization is particularly useful in the context of machine learning algorithms, where input features may have different scales. By standardizing the data, it ensures that each feature contributes equally to distance calculations and hyperparameter optimization, which can improve model convergence and performance. Additionally, Z-score normalization helps identify outliers, as the data is distributed on a common scale. This technique is foundational in various machine learning methods, including logistic regression, support vector machines, and neural networks, where the scale of the data can significantly influence the results. In summary, Z-score normalization is an essential tool in data preprocessing that allows for better interpretation and analysis of data in the field of machine learning.
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