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.
Megan Kurka suggested that we should do a tutorial/blog posts with ordinal regression. Here are the datasets recommended by her:
Credit Card Data: https://s3.amazonaws.com/h2o-training/events/ibm_index/CreditCard_Cat-train.csv Response Column = EDUCATION
Lending Club Data: in h2o-3/bigdata/laptop/lendingclub/ folder Response Column = grade
Bank data: https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv Response column: education