xgboost: Is there a way to perform regression on rates/percentages data? I have a dependent variable, $Y$, that is made up of rates/percentages data, so each value is between $0$ and $1$. I was attracted to the xgboost library because it allows focusing in on specific subsets of the data in training itself, but I am stumped as how to perform regression on the data I have.

Normal OLS regression will produce outputs that will be over and under the [0,1] range if you do not change the likelihood to be the Beta distribution, or something else bounded in the same range, but will xgboost suffer the same mistake?

Any advice on trying to get this to work would be greatly appreciated by me.
In principle: yes, you will have the same problem as with OLS. However, since xgboost is tree-based (and by that non-parametric), you may get relatively accurate estimates, meaning that values which are below zero or above one would be rare (at least the problem should be less severe than with OLS). In this case you could simply restrict results to $\hat{y} \in [0,1]$. An alternative would be to do a multiclass classification task where you have 100 classes $y=[1\%,2\%,...,100\%]$. Boosting usually performs well on classification tasks. 

Just a hint: xgboost can be somewhat "heavy" in data handling. Boosting tools such as lightgbm are a good alternative. I prefer lightgbm over xgboost.