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 |
Data Insights:
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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