CART uses strictly binary, or two-way, splits that divide each parent node into exactly two child nodes by posing questions with yes/no answers at each decision node. CART searches for questions that split nodes into relatively homogenous child nodes, such as a group consisting largely of responders, or high credit risks, or people who bought sport-utility vehicles. As the tree evolves, the nodes become increasingly more homogenous, identifying important segments. Other methods, such as CHAID, favor multi-way splits that can paint visually appealing trees but that can bog models down with less accurate splits.