
Cost Of False Positives
Cost of False Positives refers to the negative impact of incorrect predictions made by a machine learning model, where the model predicts a positive outcome when the actual outcome is negative. This can lead to missed opportunities, wasted resources, and decreased trust in the model. In some cases, false positives can have serious consequences, such as in medical diagnosis or fraud detection. The cost of false positives can be measured in terms of financial loss, missed opportunities, or damage to reputation. It is important to balance the cost of false positives with the cost of false negatives, where the model predicts a negative outcome when the actual outcome is positive.
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