Prediction Intervals for Deep Learning Neural Networks
Prediction intervals provide a measure of uncertainty for predictions on regression problems.
For example, a 95% prediction interval indicates that 95 out of 100 times, the true value will fall between the lower and upper values of the range. This is different from a simple point prediction that might represent the center of the uncertainty interval.
There are no standard techniques for calculating a prediction interval for deep learning neural networks on regression predictive modeling problems. Nevertheless, a quick and dirty prediction interval can be estimated using an ensemble of models that, in turn, provide a distribution of point predictions from which an interval can be calculated.
In this tutorial, you will discover how to calculate a prediction interval