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?