[D] What are the reasons why you standardise features for a tree based model ?

Is the below statement true? If yes, could you give a bit of explanation please.

Logistic regressions and tree-based algorithms such as decision trees, random forests and gradient boosting are not sensitive to the magnitude of variables. So standardization is not needed before fitting these kinds of models.

Found above in this link - [https://builtin.com/data-science/when-and-why-standardize-your-data](https://builtin.com/data-science/when-and-why-standardize-your-data)

Is this the same thing as saying outliers don't impact tree based algorithms ?

algorithms boosting decision decision trees features forests gradient logistic machinelearning random random forests standardization tree trees true variables