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I came across this question which asks if Azure ML can calculate confidence - or probabilities - for row data prediction. However, given that the answer to that question is No, and suggests to use R, I am trying to figure out how to use R to do exactly this for a regression model.

Does anyone have any suggestions for references on where to look for this?

My scenario is that I have used Azure ML to build a boosted decision tree regression model, which outputs a Scored Label column. But I don't know regression analysis well enough to write R code to use the outputted model to get confidence intervals.

I am looking for any references that can help me understand how to do this in R (in conjuncture with Azure ML).

1 Answer

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  1. Rebuild the model using the library(gbm) or (glm) 

  2. Build the confidence interval using confint function

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