Imbalanced data is where a lot of "99% accurate" models quietly fall apart, so good to see it treated head-on. The fraud example is perfect, because that is exactly where accuracy is the wrong metric and everyone learns it the hard way. I would love to see precision-recall tradeoffs added, since that is usually where the real decision lives.
Kartik N V J K
AI Developer | Making AI reliable, trustworthy & accessible to everyone | Active community contributor
Imbalanced data is where a lot of "99% accurate" models quietly fall apart, so good to see it treated head-on. The fraud example is perfect, because that is exactly where accuracy is the wrong metric and everyone learns it the hard way. I would love to see precision-recall tradeoffs added, since that is usually where the real decision lives.