Decision trees is a non parametric multi class classification technique. It is the oldest classification technique which has become the basis of more advanced classification techniques such as : Random forests Gradient Boosting It works on the principal of Entropy Minimization. Entropy means Chaos. Decision Trees tries to mimimize Chaos and organize things to the maximum. Decision Trees will maximize splits in a given data to get the most accurate result. Some of the commonly used terms in Decision Trees: 1. Root Node : It represents entire population or sample and this further gets divided into two or more homogeneous sets. 2. Splitting : It is a process of dividing a node into two or more sub nodes. 3. Decision Nodes : When a sub node splits into further sub nodes, then it is called Decision Node. 4. Leaf/ Terminal Node : Nodes that do not split are called Leaf or Terminal Node....