Decision Trees can be defined as the Supervised Machine Learning, where the data is continuously split according to a certain parameter. It builds classification or regression models as similar as a tree structure, with datasets broken up into ever smaller subsets while developing the decision tree. The tree can be defined by two entities, namely decision nodes, and leaves. The leaves are the decisions or the outcomes, and the decision nodes are where the data is split. Decision trees can manage both categorical and numerical data.
