I"m trying to understand how caret is coming to the decision it's making on the best-tuned model. I have looked through the documentation and I have not found (which could easily be my fault) a place to adjust how this decision is made. I'm using something similar to :
train(
y~.,
data=X,
num.trees = 1000,
method = "ranger",
trControl = trainControl(
method = "repeatedcv",
number = 100,
repeats = 100, verboseIter = T
)
I'm trying to use Caret more often, and I'm sure there is a smart way it's making the decision.. I'm just trying to understand how and if I can adjust it.
There is a lot of documentation but the best place to look for your question is here.
Basically, for grid search, multiple combinations of tuning parameters are evaluated using resampling. Each combination gets an associated resampling estimate of performance (let's say it is accuracy).
train() knows that accuracy should be maximized so, by default, it picks the parameter combination with the largest value and uses these to fit one final model (using these values and the entire training set).