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Exploring optimal cut-off points in binary classification is crucial in machine learning. This study enhances traditional methods by integrating error costs, optimizing cut-off points for predicted probabilities. Through modified discriminant functions, the approach systematically adjusts thresholds, enhancing model accuracy. Simulation comparisons with logistic and Bayesian regression frameworks reveal superior performance in logistic models using this innovative method. Practical application in finance assesses home equity default status, showcasing real-world relevance. Dive deeper into this advancement in Probability Theory Assignment Solver. To know more about us, please visit: https://www.mathsassignmenthel....p.com/probability-th

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