I have a small data set (4000 records with 10 features) and I used XGBOOST in R as well as Boosted Decision Tree model in Azure ML studio. Unfortunately the results are different. I like to optimize recall and I could pick that as a measure in Azure but I can not do so in R.

I used the same parameters in both platforms. I know seeds might be different but I tried many of them. I always have a much better recall on my validation dataset using the Azure model compared to the R one.

I wonder if there is a big difference behind the methodology used in these two platforms causing me the issues.

I also used cross validation which did not help. Any insight is appreciated.



1 Answer 1


It's hard to say, without being able to know exactly what Azure is doing.

  • From what they do share, they bin continuous features; you could try tree_method='hist' in xgb to be more similar there.
  • I can't tell how Azure deals with categoricals or missing values.
  • Be sure to set xgb's max_depth=0 and grow_policy='lossguide', since you want to use max_leaves instead for a direct comparison.


  • $\begingroup$ Thank you Ben. I did that however the model gives me the same prediction for all of my validation data. If I remove the max_depth=0 , things are back to how they were before but when I add that to the function, it does not work properly. $\endgroup$
    – Fatima
    Commented Sep 30, 2019 at 18:12
  • $\begingroup$ @Fatima, It works for me. Can you provide your code? $\endgroup$
    – Ben Reiniger
    Commented Sep 30, 2019 at 18:25
  • $\begingroup$ Here is the code that I have: bst <- xgboost(data = as.matrix(Train %>% select(-spam)), label = Train$spam, max_depth =0,max_leaves=100,grow_policy='lossguide', eta = 0.1, nrounds = 350, objective = "binary:logistic") $\endgroup$
    – Fatima
    Commented Sep 30, 2019 at 18:46
  • $\begingroup$ @Fatima Needs tree_method='hist', I think. $\endgroup$
    – Ben Reiniger
    Commented Sep 30, 2019 at 18:49

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