Description: Data rescaling is the process of adjusting the scale of data to fit a specific range. This procedure is fundamental in data preprocessing, especially in the context of machine learning and data mining. The main reason for rescaling is that many machine learning algorithms are sensitive to the scale of the data. For example, algorithms like logistic regression, support vector machines, and neural networks can be negatively affected if the input features have different scales. By rescaling the data, the goal is to normalize or standardize the features so that they contribute equally to the model. There are different rescaling techniques, such as normalization, which adjusts the data to be within a range of 0 to 1, and standardization, which transforms the data to have a mean of 0 and a standard deviation of 1. These techniques not only improve the convergence of algorithms but can also help avoid overfitting issues and enhance model interpretability. In summary, data rescaling is a crucial step in preprocessing that ensures machine learning models operate optimally and efficiently.
