Prediction intervals are commonly used for linear models but are often underused for random forests. Leveraging the fact that a random forest can provide a conditional distribution instead of just the conditional mean makes prediction intervals relatively straightforward to use in this context.
Read it!To avoid responding with "that's what Andrew NG said" when asked about the reason behind choosing an 80% training and 20% validation split, consider this explanation.
Read it!