xgboost in R have different results compared to boosted decision tree in Azure ML 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.

Thanks
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.


https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-boosted-decision-tree#usage-tips
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-boosted-decision-tree#module-parameters