Feedback loop

By providing feedback on alerts, you can improve the model accuracy.
The current qualifications are:

  • False Negative: there is an anomaly that the model has not raised
  • False Positive: the alert raised is not correct
  • Expected: the alert was expected. For instance, it can come for an extraordinary event such as a peak in sales on Black Friday sales.
  • Fixed: the alert has been fixed
  • Known Error: it is a known issue but won't be fixed as it might not be the priority

How to qualify a data point

You can qualify a data point by simply clicking on it. It will pop a qualification screen:

Examples

Example 1:
In the case below, an alert has been identified on May 1st. After investigation, the root cause has been identified by your team but won't fix it for now.
Since it will not be fixed any time soon, and in order to avoid impacting future predictions, you can set the Qualification to "Known Error".

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Example 2:
In the case below, an alert has been identified on March 3rd. After investigation, it appears that the alert was inaccurate and that the model underestimated the expected value.

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In order to improve future predictions, you can set the Qualification to "False Positive".
You can already see the impact after the next run: the model will be less likely to raise an alert by underestimating the expected value.

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