Feature Scaling

Feature scaling, the process of transforming numerical features in datasets to a common scale, is crucial for improving the performance and reliability of machine learning models. Current research focuses on developing both unsupervised and supervised scaling techniques, exploring methods like robust scaling and decision-tree-based approaches to optimize feature weighting and address inconsistencies between training and deployment data. These advancements are impacting diverse fields, from improving the efficiency of neural networks in industrial processes to enhancing the accuracy and speed of medical image reconstruction and search ranking algorithms.