What parameters are optimized by default when the crossval-on name-value pair option is used in the fitrensemble function?

For eg, when the following command is used, what parameters/hyperparamters are validated by default when the crossval-on name-value pair option is used in the fitrensemble function?

rng(1);

t = templateTree('MaxNumSplits',1);

Mdl = fitrensemble(X,MPG,'Learners',t,'CrossVal','on');

Answer 1

[Aditya Patil]

Cross validation splits the data into K partitions. Then it trains the models on the K permutations of (K - 1) sets and validates it on the remaining 1 set. For example, if you use 10-fold validation, it will train on 9 different permutations of the sets, each having 9 sets for training, and 1 for validation.

As such, there is no dependence on the parameters of the model.