Select all the correct statements about Kernel Ridge Regression $($KRR$)$:

Question options:

KRR is useful when the data is not linearly separable

KRR does not involve regularization

KRR is only applicable to linearly separable data

KRR uses a kernel function to map the data into a higher$-$dimensional feature space

KRR aims to find the weights that minimize the sum of squared errors between the predicted and actual values