A decision tree can be used in classification, prediction and regression. Decision tree model is built starting with a root node.
Training data is partitioned to the children nodes using a splitting rule.
Here A is selected variable, c is a constant, s is the data sample and L and R are the left and right branches of the node. Here splitting is done using one variable and a node has two branches and thus two children. A node can also have more branches and the splitting can be done based on several variables. The data is considered to be pure when it contains data samples from only one class. The data portion that falls into each children node is partitioned again in effort to maximise the purity function.