How are kernel hyperparameters (e.g., length scale, signal variance) typically learned in GPR?

Which of the following are valid ways to measure feature importance in Random Forest? (Select all that apply)

At each split in a Random Forest, the algorithm considers:

The out-of-bag (OOB) error in Random Forest is computed using:

How does XGBoost differ from standard Gradient Boosting? (Select all that apply)

In Gaussian Process Regression (GPR), the kernel (covariance) function defines:

What is the computational complexity of standard Gaussian Process Regression for n training points?