The first big difference is that you **calculate accuracy on the predicted classes** while you **calculate ROC AUC on predicted scores**. That means you will have to find the optimal threshold for your problem.

Moreover, accuracy looks at fractions of correctly assigned positive and negative classes. That means if our **problem is highly imbalanced **we get a really **high accuracy score **by simply predicting that** all observations belong to the majority class.**

On the flip side, if your problem is **balanced** and you **care about both positive and negative predictions**, **accuracy is a good choice** because it is really simple and easy to interpret.

Another thing to remember is that **ROC AUC is especially good at ranking** predictions. Because of that, if you have a problem where sorting your observations is what you care about ROC AUC is likely what you are looking for.

Now, let’s look at the results of our experiments: