Evaluating Performance Metrics
No model of a real-world phenomenon is perfect. There are countless statistical assumptions made about the underlying data, there is noise in the measurements, and there are unknown and unmodeled factors that contribute to the output. But even though it is not perfect, a good model is still informative and valuable. So, how do you know whether you have such a good model? How can you be sure your predictions for the future can be trusted? Cross-validation got us part of the way there, by providing a technique to compare unbiased predictions to actual values. This chapter is all about how to compare different models.
Prophet features several different metrics that are used for comparing your actual values with your predicted values, so you can quantify the performance of your model. This tells you how good your model actually is and whether you can trust the predictions, and helps you compare the performance of different models so you can choose which...
