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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).
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To find the confidence interval in R, create a new data. frame with the desired value to predict. The prediction is made with the predict() function. The interval argument is set to 'confidence' to output the mean interval.
(Usually given) If not given, use the following formula:- Margin of error = Critical value * Standard deviation Step 4: Specify the confidence interval. The uncertainty is denoted by the confidence level. And the range of the confidence interval is defined by Eq-1. Calculation of CI requires two statistical parameters.
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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
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