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.
These names in gen_R.py are inconsistent:
{code} def algo_to_modelname(algo): if algo == "aggregator": return "H2O Aggregator Model" if algo == "deeplearning": return "Deep Learning - Neural Network" if algo == "deepwater": return "Deep Water - Neural Network" if algo == "drf": return "Random Forest Model in H2O" if algo == "gbm": return "Gradient Boosting Machine" if algo == "glm": return "H2O Generalized Linear Models" if algo == "glrm": return "Generalized Low Rank Model" if algo == "kmeans": return "KMeans Model in H2O" if algo == "naivebayes": return "Naive Bayes Model in H2O" if algo == "pca": return "Principal Components Analysis" if algo == "svd": return "Singular Value Decomposition" if algo == "stackedensemble": return "H2O Stacked Ensemble" return algo {code}