In practice, GRNN is very similar to GAM (Generalized Additive Models) in the sense that they both shared the flexibility of approximating non-linear functions. In the example below, both GRNN and GAM were applied to the Kyphosis data that has been widely experimented in examples of GAM and revealed very similar patterns of functional relationships between model predictors and the response (red for GRNN and blue for GAM). However, while we have to determine the degree of freedom for each predictor in order to control the smoothness of a GAM model, there is only one tuning parameter governing the overall fitting of a GRNN model.