Symmetrical uncertainty is a measure used to quantify the amount of information shared between two variables, helping to identify their level of dependency. This concept plays a crucial role in feature selection methods by enabling the selection of the most relevant features in a dataset while minimizing redundancy. By evaluating the symmetrical uncertainty between features and the target variable, one can determine which features contribute the most informative value to the predictive model.
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