Add support for model checkpointing of the Stacked Ensemble metalearner

Description

We should add a checkpointing parameter to the Stacked Ensemble function. If you train a Stacked Ensemble with metalearner_algorithm = “GBM” or something that supports checkpointing, we should be able to start-stop the training (feature request from folks who are working on streaming/online models with H2O).

Grab the metalearner model object from the SE object and use it’s checkpointing functionality to re-start training. Then copy the updated metalearner model & metrics into all the right places (including top-level metrics for SE model).

Assignee

Tomas Fryda

Fix versions

Reporter

Erin LeDell

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