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Decision tree is a popular Supervised learning algorithm which can handle classification and regression problems. For both problems, the algorithm breaks down a dataset into smaller subsets by using if-then-else decision rules within the features of the data.
The general idea of a decision tree is that each of the features are evaluated by the algorithm and used to split the tree based on the capacity that they have to explain the target variable. The features could be categorical or continuous variables.
