Algorithms

This section provides an overview of each algorithm available in H2O. For detailed information about the parameters that can be used for building models, refer to Appendix A - Parameters.

Common

• Commonalities

Supervised

• Deep Learning (Neural Networks)
• Distributed Random Forest (DRF)
• Generalized Linear Model (GLM)
• Gradient Boosting Machine (GBM)
• Naïve Bayes Classifier
• Stacked Ensembles
• XGBoost

Unsupervised

• Generalized Low Rank Models (GLRM)
• K-Means Clustering
• Principal Compent Analysis (PCA)

Miscellaneous

• Word2vec