Train a new frozen model with parameters from an existing model.

Train a new frozen model with parameters from an existing model. Frozen models use tuning parameters from another model on the leaderboard, allowing them to be retrained on a larger amount of the training data more efficiently.
To specify the amount of data to use to train the model, use either samplePct to express a percentage of the rows of the dataset to use or trainingRowCount to express the number of rows to use.
If neither samplePct or trainingRowCount is specified, the model will be trained on the maximum available training data that can be used to train an in-memory model.
For projects using smart sampling, samplePct and trainingRowCount will be interpreted as a percent or number of rows of the minority class.

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