Which feature is chosen as the root decision node in the CART algorithm?

In the CART (Classification and Regression Trees) algorithm, the root decision node is determined by choosing the feature that results in the most effective split of the dataset, promoting the greatest distinction between different classes. This effectiveness is quantified using impurity measures, with Gini index being one of the most common.

The Gini index measures the impurity of a dataset, where a lower Gini index indicates a purer split. Therefore, the feature that leads to the lowest Gini index when the dataset is split at that feature is selected as the root decision node. By minimizing impurity and maximizing the separation of classes, CART ensures that the decisions made at each level of the tree are optimal, leading to better classification performance.

The other options refer to different approaches that do not align with CART’s methodology. The median purity, least purity, and highest Gini index do not effectively guide the selection of the root node in the context of this algorithm.