The decision tree falls under the category of supervised machine learning technique, it is also referred to as CART (Classification and Regression Trees). It utilises a tree structure to model relationships among the features and the outcomes. It consists **_nodes _**which represents decision function and **_branches _**which represent the output of the decision functions. Thus, it is a flow chart for deciding how to classify a new data point.

The decision selects the best attribute using Attribute Selection Measures(ASM) to split the records. The tree criterion splits the data into subsets and subsets into further smaller subsets. The algorithm stops splitting the data when data within the subsets are sufficiently homogeneous. The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes.

The decision tree can be used for both classification and regression problems, but they work differently.

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