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The labels below were chosen for prediction initially by the model. Only the issues which are specific to these labels are what is being tested on, in other words either the specific label being tested on was predicted by the model or the specific label was the actual label on the issue. This score Results in accurately predicting a label – Meaning The accuracy shown below denotes where 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 ***

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Classification report with precision, recall, and f1 score:

Data Insights:

Motivations/Conclusion:

This shows us which labels which we can provide by the model given a certain accuracy threshold. The bot would help in being able to determine at least one label to new issues but may not always be able to deliver all the labels that are associated with an issue.