How does xgboost split root node and question for Taylor expansion

Question

I know xgboost use Gain = Score(L)+Score(R)-Score(L+R) to split node, but how does xgboost split root node? Also, why not use the fourth or fifth derivative in Taylor expansion for loss function?

Answer 1

Before root node, There is a initial value nameed 'base_score' (default 0.5 for classification) as a predict value, so in root node, you can calculate all samples' hessian and gradient and obtain the score for gain.