Precision, recall and the threshold
Two modes in one widget. Edit TP, FP, FN and TN directly and every metric updates; or take a fixed labelled dataset and drag the decision threshold while a live dot walks the precision–recall and ROC curves.
Try this
Make the negative class 100× larger than the positive one and watch accuracy stay high while precision falls apart.
Where this lab comes from
Classification Metrics, From the Ground Up: Precision, Recall, F1, ROC and AUCThe confusion matrix and everything that falls out of it: precision, recall, F1, specificity, the precision/recall trade-off, PR and ROC curves, and AUC, all built on one running toy example (a backyard cat-cam), with the equations, the intuition, and scikit-learn.
More in metrics
- IoU and Dice, by dragging boxes — Two draggable boxes; intersection, union and both scores update live.
- Macro, micro and weighted, side by side — An editable C×C confusion matrix with per-class F1 drawn as bars.
- When one photo carries several labels — Toggle predicted tags and watch subset accuracy punish you.