video1.0<iframe src="https://www.loom.com/embed/f3d2e099873d4a0f8c0ca91e1d369900" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/f3d2e099873d4a0f8c0ca91e1d369900-4e387084f321250e.gif166.494In this video, I discuss the importance of using weight of evidence for categorical variables in fraud detection. I explain how this method helps us avoid overfitting by providing a numerical encoding associated with fraud ratios. It's crucial that we perform these calculations solely on the training set to prevent data leakage. I also emphasize the need to maintain the integrity of unseen data for accurate model performance. Please take note of this approach as we move forward.