training deep leaning models figure - roc curve egression metrics performance metrics for regression are fairly straightforward when compared to metrics for classification the most common metric that can be universally applied to most use cases is the mean squared error (msedepending on the use casea few other metrics could be used for more favorable outcomes consider the problem of predicting the monthly sales for given storewhere store sales could range from $ , to $ , across months the following sections explore few popular choices mean squared error we have already explored the mean squared error (msein "feed-forward neural networks as the name suggeststhe mse is the mean of the squared differences between the actual values and the