# Can log loss be an evaluation metric for classification models?

I read several posts online about evaluation metrics for classification models. Only accuracy, precision, recall, F-1 score, ROC, AUC, Confusion matrix are mentioned. However, I found a couple of Kaggle competitions use log loss as the evaluation metric. For example, Dogs vs. Cats Redux: Kernels Edition.

A standard reason to prefer a metric like accuracy is that it seems easy to interpret. "I got an accuracy of $$95\%$$, so that's like an $$\text{A}$$ in school, and I am happy." I would argue that accuracy has to be evaluated in context. A standard way that an accuracy of $$95\%$$ might be poor performance is if $$99\%$$ of the cases belong to one class, which means that you could get a higher accuracy just by predicting the majority class every time.