Description: Mean centering is a fundamental preprocessing step that involves subtracting the mean from each data point in a dataset. This process aims to adjust the data so that its mean is zero, facilitating analysis and modeling. By centering the data, biases that may influence the results of machine learning algorithms and statistical analysis are removed. This method is particularly relevant in techniques sensitive to data scaling, such as principal component analysis (PCA) and various regression methods. Centering the data improves the convergence of algorithms and optimizes the overall performance of the model. Additionally, mean centering helps to highlight patterns and relationships in the data, allowing for better interpretation of results. Mean centering can be efficiently implemented using distributed processing capabilities, enabling the handling of large volumes of data without compromising performance.
