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.

YES, this is a reasonable evaluation metric. In particular, log loss is a strictly proper scoring rule that is maximized in expected value by the true probability values.
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.
Consequently, I do not see accuracy as easy to interpret, and I do not buy the argument to prefer accuracy over a strictly proper scoring rule like log loss due to the ease with which accuracy can be interpreted.
