<ul data-eligibleForWebStory="false"><li>Binary classification involves predicting which of two classes a particular instance belongs to, often represented as 0 and 1, 'yes' and 'no', or 'true' and 'false'.</li><li>Binary classification outcomes are typically represented in a confusion matrix, detailing true positives, false positives, true negatives, and false negatives.</li><li>Local sensitivity analysis involves examining partial derivatives, while global sensitivity analysis using the Sobol method provides insights into overall model performance.</li><li>The research delves into understanding the costs associated with binary classification operations and emphasizes the importance of considering different cost scenarios for accurate model evaluation.</li></ul>

Exploring Local and Global Sensitivity in Binary Decision Modeling

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