H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
AutoML Py API could benefit from standards used in the rest of h2o-3 Python API:
property accessors with docstring mentioning their backend default value (see if this part could be generated from AutoML schema like the algos estimators are).
no hardcoding of default parameter values on client-side (backend should be the single reference for default values): this is usually done by defaulting all params to None and by sending them to backend only if provided by user.
AutoML Py API could benefit from standards used in the rest of h2o-3 Python API: