A decision tree is based on the recursive partitioning approach (divide and conquer), which generates a set of rules that can be used to predict a label. It starts with a root node and splits into multiple branches. Internal nodes represent a test on a certain attribute and the result of the test is represented by a branch to the next level. The decision tree ends in leaf nodes that contain the decisions. The process stops when partitioning no longer improves the outcome.