Entropy In Decision Tree Table Of Contents: What Is Entropy? Interpreting Entropy. Formula For Entropy. How Decision Tree Uses Entropy? (1) What Is Entropy? Entropy is a concept used in the context of decision trees to measure the impurity or randomness of a set of examples within a particular node. In Decision Tree algorithms, Entropy is used as a criterion to determine the best feature for splitting the data. In the context of decision trees, entropy is calculated based on the distribution of class labels within a node. If a node contains only examples from a single class, it is

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