Question

A data scientist launches a Databricks AutoML forecasting experiment through the UI. They notice that AutoML asks them to select a 'time column' but does not offer options to manually configure a random train/validation/test split percentage the way they expect from a classification experiment. Why does AutoML handle the data split differently for this forecasting experiment?

Accepted Answer

For forecasting, AutoML splits the data chronologically based on the time column so that validation and test sets always come after the training period, preserving temporal order.

Answer

For forecasting, AutoML always uses the entire dataset for training and skips validation entirely, so no split configuration is exposed.

Answer

For forecasting, AutoML randomly shuffles rows and applies an 80/20 split identical to classification, but hides the setting to reduce clutter.

Answer

For forecasting, AutoML requires the user to pre-split the data into separate tables before starting the experiment, which is why no split UI appears.