video1.0<iframe src="https://www.loom.com/embed/8652ca1268c94649a77295f4041f72ff" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/8652ca1268c94649a77295f4041f72ff-29bb3fa5f37855fa.gif874.772In this video, I dive into the crucial aspects of model evaluation, focusing on precision and recall curves. I explain how to balance these metrics to optimize fraud detection, aiming for a precision of around 70-80%. I also discuss the financial implications of false positives and false negatives, highlighting a potential loss of $15 million due to misclassifications. Please take a moment to review the calculations and insights I present, as your feedback will be valuable for our next steps.