# Comparing Supervised ML algorithms in R on same data set

I've recently embarked on my data science journey, and I've therefore also started a data science course.

In this course, we've received an assignment asking us to model a data set using different supervised algorithm (logistic regression, SVM, classification trees, random forest).

Once we've built the models, we're asked to compare them. I get the theoretical pros and cons (blackbox, accuracy, etc.).

My question, however, is related to comparing logistic regression to the remaining algorithms. In the remaining algorithm I get an accuracy through classification tables as I've utilized training/testing sets. These are easily comparable. This has not been done with the logistic regression, as I've not been taught how to in the course.

So, my question is: How do i compare logistic regression to the remaining supervised algorithms?