Which of the following statements regarding support vector machines $($SVM$)$ and random forests $($RF$)$ is true?

Question $3$ options:

SVM is less prone to overfitting compared to RF$.$

RF is more computationally efficient than SVM$.$

SVM is generally better than RF for handling high$-$dimensional data.

RF performs better than SVM when the dataset is imbalanced.