In this chapter, we'll showcase how to build heterogeneous ensemble classifier using H2O, which is an open source, distributed, in-memory, machine learning platform. There are a host of supervised and unsupervised algorithms available in H2O.
Among the supervised algorithms, H2O provides us with neural networks, random forest (RF), generalized linear models, a Gradient-Boosting Machine, a naive Bayes classifier, and XGBoost.
H2O also provides us with a stacked ensemble method that aims to find the optimal combination of a collection of predictive algorithms using the stacking process. H2O's stacked ensemble supports both regression and classification.
