Minimizing Misclassification Cost | Threshold Selection in Classification

In this video, we solve a classification problem where different types of errors have different costs. You’ll learn: How to assign costs to false positives (FP) and false negatives (FN) How to choose the optimal classification threshold How to minimize the overall expected misclassification cost A step-by-step example with real numbers (FP = $10, FN = $5) This is a practical example that shows how cost-sensitive learning improves decision-making beyond accuracy alone. #MachineLearning #Classification #Threshold #CostSensitiveLearning #DataScience #exampreparation