Lightgbm confidence interval I want to compute a confidence interval for each sample for a lightgbm model I've trained.
If the model was a random forest, it'd be quite easy, just take all the trees and compute the standard deviation of the predictions.
I wonder if a similar think can be done with lightgbm. In particular:

Does it make sense to compute the standard deviation of a boosting model by taking the predictions of all trees, or something similar?
Can it be done in scikit-learn?
Are there alternatives to obtain confidence intervals for lightgbm predictions, appart from the alternative that would be training quantile models?
If you are looking for a statistical trick, I don't know, but
Recently Andrew NG team recently published about NGBoost.

NGBoost is a new boosting algorithm, which uses Natural Gradient Boosting, a modular boosting algorithm for probabilistic predictions.

In this Towards Data Science toy example you can see how to use the Python API:
Quoting the TDS author:

NGBoost’s one of the biggest differences from other boosting algorithms is can return probabilistic distribution of each prediction. This can be visualized by using pred_dist function.


In their web they say &quot;NGBoost can be used with any base learner, any family of distributions with continuous parameters, and any scoring rule.&quot; So it seems pretty flexible to do whatever you consider.