Question

A data scientist launches a Databricks AutoML forecasting experiment through the UI on a table of daily retail sales. After the experiment completes, they want to understand how far into the future the best model can predict and how AutoML determined the number of future periods to generate. Which combination of AutoML forecasting configuration settings controls the number of future time steps produced by the resulting model?

Accepted Answer

The forecast frequency (e.g., daily) and the forecast horizon, which together define how many future periods are generated

Answer

The primary metric and the timeout, which cap how many future periods the model can produce

Answer

The number of trials and the training/validation split ratio, which determine the future window size

Answer

The time column data type and the country holidays setting, which set the prediction length automatically