Description: Rescaling is the process of transforming features to be on a similar scale, often between 0 and 1. This procedure is fundamental in data preprocessing, especially in the fields of machine learning and data mining. The main reason for applying rescaling is that many machine learning algorithms, such as distance-based ones (e.g., k-nearest neighbors) or gradient-based ones (like logistic regression), are sensitive to the magnitude of the features. If features have different scales, those with higher values can dominate the learning process, leading to suboptimal results. There are several rescaling techniques, with normalization and standardization being the most common. Normalization adjusts values to fall within a specific range, while standardization transforms data to have a mean of zero and a standard deviation of one. Rescaling not only improves model accuracy but also accelerates the convergence process during training. In summary, rescaling is an essential technique that allows machine learning models to operate more efficiently and effectively, ensuring that all features contribute equally to the learning process.
