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The Gini index measures statistical impurity or inequality and is commonly used in machine learning as an impurity measure in decision tree algorithms for classification tasks. The formula involves the number of classes and the probabilities associated with each class. This concept is critical for understanding decision-making processes in both statistical and monetary contexts.
