Create a custom AutoML strategy for classification with a high number of classes

Description

We should consider a re-ordering of the algorithms when there are a high number of classes in the response. For example, perhaps >10 classes, we switch to prioritize GLMs and DNNs over tree-based methods.

Benchmark results on MNIST: https://www.kaggle.com/tunguz/mnist-with-h2o-automl/

Environment

None

Status

Assignee

Unassigned

Fix versions

None

Reporter

Erin LeDell

Support ticket URL

None

Labels

None

Release Priority

None

Affected Spark version

None

Customer Request Type

None

Task progress

None

CustomerVisible

No

Components

Priority

Major