Data exploration is not important in AutoML, as the algorithm automatically handles all aspects of the machine learning workflow.

Data exploration is important in AutoML to identify key attributes of a dataset, but the AutoML data exploration notebook is not useful for this purpose.

Data exploration is important in AutoML to identify key attributes of a dataset, and the AutoML data exploration notebook can be used to perform various analyses, such as statistical summaries, data visualization, and feature importance analysis.

The AutoML data exploration notebook is only useful for visualizing the dataset, but not for identifying key attributes or performing any other analyses.