i am using the caret package to train my gam model. my code looks like this
gam.train<- train(price ~ . , data=data, method = "gam",
family = Gamma(link = log))
and my output looks like this
Tuning parameter 'method' was held constant at a value of GCV.Cp
RMSE was used to select the optimal model using the smallest value.
The final values used for the model were select = TRUE and method = GCV.Cp.
Now I just want to ask you guys if my interpretation is correct:
- I am tuning 2 parameter: methods and select
- method stands for "smoothing parameter estimation" method: GCV.Cp, REML, GACV.CP
- select means, that it shrinks my coefficients to almost 0, not like a backward selection with AIC or CP
- it tunes those parameter via cross validation using RMSE to choose the "best" parameter
- it also chooses which variables will be modelled as functions and which one as linear
did i get that correctly? Nevertheless, i still have some question
- why isn't it tuning the splines? when i look into the gam package, it uses the "thin plate splines" as default
- is it also tuning the degree of freedom of my smoothing terms? i guess yes, but i can't see that in the output
- since i used gamma as family: does it also tune the parameter for my gamma distribution?
i really hope that some of you guys can help me with this and i will really appreciate every answer!!!!!