Exploring Dataset Details in Driverless AI

This lesson walks through the Dataset Details view in H2O Driverless AI using a credit score dataset. It covers how the Details button shows descriptive statistics, storage types, and lets you change a feature's logical type, while the Visualize option gives a distribution chart for each column. It uses the Credit_Score target column as an example, showing that about 71 percent of applications are labeled good and 29 percent poor, and explains why an insurance company or bank cares about predicting this accurately. It also covers the search bar for filtering columns and the Dataset Rows option for previewing the raw data before modeling.