What is the difference between Gini Impurity and Entropy in a Decision Tree?

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February 3, 2024September 13, 2020 by priya

Gini Impurity and Entropy are the metrics used for deciding how to split a Decision Tree.
Gini measurement is the probability of a random sample being classified correctly if you randomly pick a label according to the distribution in the branch.
Entropy is a measurement to calculate the lack of information. You calculate the Information Gain (difference in entropies) by making a split. This measure helps to reduce the uncertainty about the output label.