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).
There isn't a straight forward way to compute the confidence interval from the results of the Boosted Decision Tree model in Azure ML.
Here are some alternate suggestions:
Rebuild the model using the library(gbm) http://artax.karlin.mff.cuni.cz/r-help/library/gbm/html/gbm.html or the library(glm) https://stat.ethz.ch/R-manual/R-devel/library/stats/html/glm.html
Then build the confidence interval using confint function: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/confint.html
For a linear model, the confidence interval computation is simpler: http://www.r-tutor.com/elementary-statistics/simple-linear-regression/confidence-interval-linear-regression