Question

A data scientist runs a Databricks AutoML classification experiment on a highly imbalanced dataset where only 4% of records belong to the positive (fraud) class. After the experiment completes, they open the AutoML UI to evaluate the trained models. Which behavior should they expect from AutoML regarding how it handles this imbalanced dataset and reports its results?

Accepted Answer

AutoML automatically detects the class imbalance, applies a balancing technique to the majority class in the training data, and surfaces this in the experiment's data exploration notebook.

Answer

AutoML requires the user to manually oversample the minority class before starting the experiment; otherwise it aborts with an error.

Answer

AutoML only supports balanced datasets and will silently drop the experiment if the class ratio exceeds a threshold.

Answer

AutoML ignores class distribution entirely and evaluates all models solely by overall accuracy, with no imbalance handling.