Suppose you have a dataset with p$=20$ features and n$=10,000$ examples. After training a logistic regression model, you find that it has underfit the training set and does not achieve the desired testing performance. Which of the following might be promising steps to take? $($Select all that apply$)$

$1$: Use an SVM with a Radial Basis Kernel.

$2$: Include a regularization term.

$3$: Include new polynomial features in the logistic model.