Pros:

• With GAMS, one can fit non-linear functions to each predictor to automatically model non-linear relationships that standard linear regression will miss
• The non-linear fits can make more accurate predictions for the response Y
• The additive nature of GAMs means we can still examine the effect of each predictor variable on Y while holding other variables fixed.
• Degrees of freedom can summarize the smoothness of the GAM

Cons:

• The main limitation is that GAMs are restricted to be additive, meaning important interactions can be missed. However, as with regular linear regression, we can manually add interactions by including additional predictors.