Information gain is a metric that measures the reduction of uncertainty or entropy when new information is acquired. It quantifies how much knowing the value of a feature improves our understanding of the outcome variable, often used in decision tree algorithms to determine the best features for splitting data. This concept connects deeply with relative entropy, mutual information, and how information flows in stochastic processes, as well as its practical applications in feature selection and dimensionality reduction.
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