A Decision Tree is a Supervised Machine Learning Algorithm that uses a set of rules to make decisions, similar to how humans make decisions. In Decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision-making. Let us take a daily life example, you want to decide whether you should accept a new job offer or not. Then, in such a case building a decision tree simplify your task and help you make the correct decision.

At their core, decision tree models are nested if-else conditions. We can use a Decision tree classifier for many tasks such as classifying which flower it is, which animal it is, whether someone is eligible for a job or not, and many more. If you have a large dataset, a Decision tree may not be a good option.

Thus, a Decision Tree has a flowchart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label. It works for both categorical and continuous dependent variables.

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