Confidence rating for regression tasks
There are many approaches to evaluate the uncertainty of your regression algorithms.
There are model dependent approaches as which the bayesian approach allows you to have an predictive distribution instead of an output which allows you to evaluate your confidence interval. You can apply this approach on linear models as well as on DNN.
There are also model free approach as bootstrapping or quantile regression that allows you to evaluate your uncertainty while considering the prediction algorithm as a black-box model