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I'm not an expert, but I think this is a feature of those models in R moreso than the Alteryximplementation thereof. I've attached a workflow that I used to play around with it a bit; it uses the Kaggle Titanic data (since it's small and fits the bill in terms of generating NULL predictions). In it's current state, everything is cleaned up so that missing values are either imputed or excluded as features of the model.
In particular, I saved a copy of the Score tool (which is just a macro - you can right-click it to look at it and see the R code), and commented out several lines of R where they explicitly generate log messages if/when NA values are removed. When scoring with either macro, it still always came out exactly the same, which, again, leads me to think it's more to do with R than Alteryx. I also Googled it just a bit in hopes of finding a definitive statement on the matter, but nothing jumped out immediately from that brief effort.
Anyway, hope that helps at least a little. Aside: it also helps to enable logging and look at them closely.
I was actually about to respond with essentially the same answer as @DylanB, but he beat me to the punch! It's also good to know that the Boosted Model tool isn't the only Predictive tool that can handle missing data. The Decision Tree and Naive Bayes classifier also have built-in R procedures to handle missing data.