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Xgboost Regression as a predictive macro

XGboost regression is now the benchmark for every Kaggle competition and seems to consistently outperform random forest, spline regression, and all of the more basic models. For those of us using predictive modeling on a regular basis in our actual work, this tool would allow for a quick improvement in our model accuracy. And I think, from a marketing standpoint, having a core group of users competing in Kaggle using Alteryx would be a great way to show off Alteryx's power.


It is readily available as an R package:

9 - Comet



Thank your for sharing with me!

15 - Aurora

From my angle (submitter of the idea), this could be marked as completed/implemented: the gallery solution given by @TimothyL is great.



8 - Asteroid

XGBoost, Catboost and LightBGM are seriously needed.

13 - Pulsar

great work here!

Status changed to: Inactive

The status of this idea has been changed to 'Inactive'. This status indicates that:


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Status changed to: New Idea

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6 - Meteoroid

I am recommending/requesting that Alteryx add an XG Boost tool written in R to the Predictive toolset. I have just finished productionizing a classification model using the Boosted tool and while it performs well, I derived better predictions using XGBoost (in another software on the same data). I am aware that the Intelligence Suite now has XGB in it...but that is at an additional cost and, quite frankly, after having tested it, more difficult to productionize.


A couple of points:

1. Every other toolset that I have used has an XGBoost algorithm as part of the standard package (SPSS Modeler, Statistica, RapidMiner);

2. XGoost is arguably the leading algorithm out there for many/most classification problems; it has been in the winning solution in a disproportionate number of Kaggle competitions (40%-50%?);

3. It is a bit gut-wrenching to tell my colleagues, "No, there is not a supported XGB tool in Alteryx" when that is seen as a litmus test for a DS platform or tool.

4. Yes, we can use R or Python and that is precisely what I will do in iteration 2 of the model...but having the tool already exist would save significant time, especially when building and testing models.


Thank you for the consideration!



13 - Pulsar
13 - Pulsar

Hi @bkramer66_dup_418 


I agree it would be nice to have an XGBoost as part of the native R predictive tools, given it's growing popularity. However, I'm pleased to let you know that there are two ways to build XGBoost models in Alteryx out of the box:


1) Using Alteryx's new Intelligence Suite, where XGBoost is one of the four model options:



2) Using an opensource macro published to the Alteryx Gallery by @TimothyL - I haven't used it, so can't verify it's success, but I suggest you give it a go:


More predictive tools that are not natively installed can be found here:!districts/56327e37aa690a17f0760bdc




6 - Meteoroid

Hi Joe,


Thank you for the comment and suggestions. As I mentioned in my original post, I tried the Intelligence Suite and actually used XGB and of course, it outperformed the other three algos. The challenge is that 1) I ran into a bug that we couldn't resolve, 2) it comes at an additional $2K+ price tag, and 3) because of the way it works (guided data cleansing and feature selection), I was leery of trying to productionize it. And of course to do so, we would have had to bite the bullet on procuring the suite. Don't get me wrong - I am anxious for the IS to develop and will probably advocate for its procurement in the next iteration or two.


Secondly, I have run the opens source macros from Timothy (who is awesome) in both R and Python. I could never get the Python one to run, largely because of our environment. The R package ran well but the predictions were significantly different from what the IS produced as well as from what the Boosted model produced. And I mean significantly, as in almost opposite probabilities. I reached out to Timothy directly and we had some communication around it but it remains unresolved on why the predictions are so disparate from what the IS and SPSS Modeler produced. I thought it may be reading the label backwards, or the probabilities are reversed or something...but who knows. I am grateful for what is out there but I think Alteryx needs to invest some development in making an R-based one more robust.


Thanks for the other tools not natively installed - I do have numerous ones (Model Comparison, TS Factory) installed and that I have used.


Thanks again for the comment and for your contributions to the community - I have read many of your posts and they have been so helpful!