# Evaluating performance of classifier on lopsided dataset

I have a binary classifier that I would like to evaluate the performance of. It's been both trained and tested on a data set where the ratio of true to false labels is lopsided. This means that while it's quite poor at correctly guessing true, its overall performance on the test set looks very good when using a metric such as right_guesses/total.

What is a better metric to use? Preferably, one where the true false labels account for the same percentage of the score although their numbers are unequal.

sklearn has weighted accuracy score which works just fine:

sklearn.metrics.balanced_accuracy_score()