THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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| Label | Precision | Recall | F1 Score | Count |
| Performance | 100% | 100% | 100% | 87 |
| Test | 99.59% | 100% | 99.8% | 245 |
| Question | 100% | 97.02% | 98.49% | 302 |
| Doc | 100% | 90.32% | 94.92% | 155 |
| Installation | 100% | 84.07% | 91.35% | 113 |
| Example | 100% | 80.81% | 89.39% | 99 |
| Bug | 100% | 78.66% | 88.06% | 389 |
| Build | 100% | 69.87% | 82.26% | 156 |
| onnx | 80% | 84.21% | 82.05% | 23 |
| scala | 86.67% | 75% | 80.41% | 58 |
| gluon | 62.28% | 60.68% | 61.47% | 160 |
| flaky | 96.51% | 43.46% | 59.93% | 194 |
| Feature | 32.43% | 98.18% | 48.76% | 335 |
| C++ | 55% | 38.6% | 45.36% | 75 |
| ci | 48.39% | 40.54% | 44.12% | 53 |
| Cuda | 22.09% | 100% | 36.19% | 86 |
Precision here representing how accurate our classifier was in correctly labelling an issue given all the times it had predicted that label.
Recall here representing how accurate our classifier was in correctly labelling an issue given all the times the issue actually had that label.
Motivations/Conclusion:
We are able to see which labels the model can predict accurately for. Given a certain accuracy threshold, the bot has the potential to label an issue given that it surpasses this value. As a result, we would be able to accurately provide labels to new issues.
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