R Decision Trees - A Tutorial to Tree Based Modeling in R

One of the most intuitive and popular methods of data mining that provides explicit rules for classification and copes well with heterogeneous data, missing data, and nonlinear effects is decision tree. It predicts the target value of an item by mapping observations about the item. You can perform either classification or regression tasks here. For example, identifying fraudulent transactions using credit cards would be a classification task while forecasting prices of stock would be regression task. Decision tree technique is used to detect the criteria for dividing individual items of a group into n predetermined classes (Often, n 2 represents a balanced tree, which means a largest of two child nodes for each parent node.) Firstly, a variable is taken as the root node.