How to measure confidence in prediction? I built a prediction model and predicted on new data. I now want to specify a value of my confidence in this predicted value, e.g. ranging from 0 to 1.
Three methods come to my mind


Built 100 models on bootstrapped data, predict 100 times on each new observation, then calculate confidence intervals. Smaller intervals mean higher confidence. High computational effort.
use oob predictions of a random forest from every tree
Bayesian methods can give confidence intervals through the posterior


Are there other/better ones?
If you've got enough information on the underlying distributions of your data, classical statistical theory on regression models is an obvious alternative. These models always come with nice derivations of error estimates. See Frank Harrell's "Regression Modelling Strategies" for a comprehensive overview.