figure - decision boundary of tree with depth (leftand corresponding decision tree (rightthis recursive process yields binary tree of decisionswith each node containing test alternativelyyou can think of each test as splitting the part of the data that is currently being considered along one axis this yields view of the algorithm as building hierarchical partition as each test concerns only single featurethe regions in the resulting partition always have axis-parallel boundaries the recursive partitioning of the data is repeated until each region in the partition (each leaf in the decision treeonly contains single target value ( single class or single regression valuea leaf of the tree that contains data points that all share the same target value is called pure the final partitioning for this dataset is shown in figure - supervised machine learning algorithms