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A decision tree is a supervised learning algorithm that is used for both classification and regression. Hence, in this case, the dependent variable can be both a numerical value and a categorical value. Here, each node denotes the test on an attribute, and each edge denotes the outcome of that attribute, and each leaf node holds the class label. So, in this case, we have a series of test conditions which give the final decision according to the condition.
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