Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer

Splines offer a flexible yet disciplined method for modeling non-linear data, avoiding the instability and overfitting issues of high-degree polynomials. The technique involves breaking data into segments at points called "knots" and fitting simpler, localized polynomials to each segment. Scikit-Learn's `SplineTransformer` can be used in a pipeline with a linear model like Ridge regression to implement this approach. The guide provides a Python code example demonstrating how to tune the number of knots with `GridSearchCV` and discusses practical applications for time-series and dose-response modeling.