Available in: Stacked Ensembles, AutoML
H2O’s Stacked Ensemble method is supervised ensemble machine learning algorithm that finds the optimal combination of a collection of prediction algorithms using a process called stacking (or Super Learning). The algorithm that learns the optimal combination of the base learners is called the metalearning algorithm or metalearner.
blending_frame parameter is used to specify a frame to be used for computing the predictions that serve as the training frame for the metalearner. If provided, this triggers blending mode. Blending mode is faster than cross-validating the base learners (though these ensembles may not perform as well as the Super Learner ensemble). In addition, a blending frame adds the ability to train stacked ensembles on time-series data, where holdout data is “future” data compared to “past” data in training set.