Generalized additive models (GAM's) can be tricky to fit due to numerical stability issues and model complexity. A new method using ridge penalty helps address these challenges by improving error propagation and handling rank deficiency. This method allows for efficient selection of smoothing parameters, even in cases with indeterminacy in model likelihood. The approach compares favorably to existing methods in terms of computational efficiency and accuracy.