Description: Standardization is a statistical process that involves adjusting the values in a dataset so that they have a mean of zero and a standard deviation of one. This procedure is fundamental in data analysis, as it allows different variables to be comparable to each other, eliminating measurement units and scales that could distort results. Standardization transforms the original data into z-scores, which indicate how many standard deviations a value is above or below the mean. This approach is particularly useful in various analytical contexts, including machine learning algorithms, where input features need to be on a similar scale to ensure optimal performance. Additionally, standardization helps improve the convergence of optimization algorithms, facilitating the training of more accurate and efficient models. In summary, standardization is an essential technique for data preparation, allowing for better interpretation and analysis in various statistical and analytical applications.
