The choice of the number and location of knots is crucial for the success of spline regression. Too few knots may result in underfitting, while too many knots can lead to overfitting. Common methods for selecting knots include: - Pre-specified Knots: Based on prior knowledge or subject-matter expertise. - Data-driven Methods: Techniques like cross-validation or information criteria (e.g., AIC) to optimize the number and location of knots. - Equally-spaced Knots: Dividing the range of the independent variable into equal intervals.
