Metrics for presenting RNN/LSTM result I am working on a two different architecture based on LSTM model to predict the users next action based on the previous actions. I am wondering, what is the best way to present the result? Is it okay to present only the prediction accuracy? Or Should I use other metrics? I found a paper using top_K_accuracy where on a different paper I found AUC of ROC. Overall, I want to know what is the state of art of presenting prediction accuracy based on LSTM model.
There really is nothing special about LSTMs when it comes to classification and metrics. So your question should be what metrics are good for multi-class classification. Both accuracy and auc works. Another metric that is good here is Matthew's Correlation Coefficient (MCC) which is similar to F1 score for a binary problem.