THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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Results in accurately predicting a label – Meaning the model predicted a label and that was one of the actual labels in the repo:
*** The accuracy metric was collected using sklearn's accuracy_score method ***
Classification Accuracy:
| Label | Accuracy | Issue Count |
| Performance | 100% | 87 |
| Test | 99.59% | 245 |
| Question | 97.02% | 302 |
| Doc | 90.32% | 155 |
| Installation | 84.07% | 113 |
| Example | 80.81% | 99 |
| Bug | 78.66% | 389 |
| Build | 69.87% | 156 |
| onnx | 69.57% | 23 |
| scala | 67.24% | 58 |
| gluon | 44.38% | 160 |
| flaky | 42.78% | 194 |
| Feature | 32.24% | 335 |
| C++ | 29.33% | 75 |
| ci | 28.30% | 53 |
| Cuda | 22.09% | 86 |
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Full classification report with precision, recall, and f1 score:
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