What is feature transformation and why is it important in supervised learning?

Feature transformation converts raw data into a more useful representation for learning algorithms.

Feature transformation involves applying mathematical or statistical operations to raw features to improve model performance. Techniques include logarithmic scaling, polynomial features, normalization, and embedding transformations. It helps linear models capture nonlinear relationships and improves convergence in gradient-based methods.