Decision Tree

Decision Tree is a supervised algorithm used in machine learning. It is using a binary tree graph (each node has two children) to assign for each data sample a target value. The target values are presented in the tree leaves. To reach the leaf, the sample is propagated through nodes, starting at the root node. In each node a decision is made, to which descendant node it should go. A decision is made based on the selected sample’s feature. It is usually one feature used to make the decision (one feature is used in the node to make a decision). Decision tree learning is a process of finding the optimal rules in each internal tree node according to the selected metric.