Description: Value range adjustment is the process of modifying the range of values in a dataset to meet specific criteria. This procedure is fundamental in data preprocessing, as it allows for the normalization or standardization of data to make it more comparable and useful in subsequent analyses. By adjusting the range of values, the goal is to transform the data so that it fits within a defined interval, such as [0, 1] or [-1, 1]. This is particularly relevant in contexts where various algorithms, including machine learning algorithms, require data to be within a specific range to function optimally. Range adjustment may involve techniques such as normalization, which scales the data to a specific range, or standardization, which transforms the data to have a mean of zero and a standard deviation of one. These techniques help mitigate issues such as the dominance of features with larger scales and facilitate the convergence of optimization algorithms. In summary, value range adjustment is a crucial step in data preprocessing that enhances the quality and effectiveness of data analysis.
