The documentation in standardize section https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/standardize.html only includes these algorithms: Deep Learning, GLM, GAM, K-Means.
I have two questions:
Does it mean that other algorithms such as Random Forest, Gradient Boosting, etc, are not standardizing (at least automatically in AutoML)?
Does standardize = TRUE in Deep Learning, GLM, ..., standardize the target variable altogether, or only features?
A related question is Feature Standardize in AutoML H2O.
Regarding your question 1. Correct. For algorithms that do not have the standardize parameter, the predictors are not standardized. For tree based algorithms, we are dealing with comparisons like val >= threshold to determine which side of the child nodes to go to. If we implement standardization, we will have to perform (val-mean)/standard deviation >= threshold. In choosing not to standardize will say us a lot of time during the tree traversal because we don't need to perform standardization of the predictors when we are trying to evaluate the expression val >= threshold.
Regarding question 2: When you set standardize=true, only the numerical features are standardized. The response column is not standardized.