Timeline for Precision vs. Recall
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 22, 2019 at 19:52 | vote | accept | FrancoSwiss | ||
| Feb 22, 2019 at 18:40 | comment | added | HFulcher | @FrancoSwiss happy to help! A high ROC score doesn't necessarily mean that your model has succeeded in dealing with one of the labels well. You can see from the F1 scores that the model is heavily biased towards predicting 0 due to the imbalance in the training set. I don't know the constraints of your dataset so this could very well be a success in this context! If you feel that I have answered your question sufficiently please mark it as answered, otherwise I would be happy to elaborate :) | |
| Feb 22, 2019 at 18:25 | comment | added | FrancoSwiss | Thank you for your explanation HFulcher! This helps a lot. Poor label? It's 35 million entries with 0.25% label 1. AUROC 0.97. I would call that a success. | |
| Feb 22, 2019 at 17:11 | history | answered | HFulcher | CC BY-SA 4.0 |