Threshold adjustment refers to the process of changing the decision boundary in a machine learning model, typically to improve performance metrics or achieve fairness. By adjusting the threshold, you can influence the trade-off between true positive rates and false positive rates, which is crucial in contexts where different outcomes can have varying impacts on different groups, especially concerning fairness and equity.
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