Should-Read; Andrew Gelman: Using black-box machine learning predictions as inputs to a Bayesian analysis: “We started by using the output from the so-called machine learning as a predictor… http://andrewgelman.com/2017/09/20/using-black-box-machine-learning-predictions-inputs-bayesian-analysis/

…then we fit a parametric model to the machine-learning fit, and now we’re transitioning toward modeling the raw data. Some interesting general lessons here, I think. In particular, machine-learning-type methods tend to be crap at extrapolation and can have weird flat behavior near the edge of the data. So in this case when we went to the parametric model, we excluded some of the machine-learning predictions in the bad zone as they were messing us up…