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This PR surfaces new information about the impact of the factors on the initial anomaly score in the anomaly record: - single bucket impact is determined by the deviation between actual and typical in the current bucket - multi-bucket impact is determined by the deviation between actual and typical in the past 12 buckets - anomaly characteristics are statistical properties of the current anomaly compared to the historical observations - high variance penalty is the reduction of anomaly score in the buckets with large confidence intervals. - incomplete bucket penalty is the reduction of anomaly score in the buckets with fewer samples than historically expected. Additionally, we compute lower- and upper-confidence bounds and the typical value for the anomaly records. This improves the explainability of the cases where the model plot is not activated with only a slight overhead in performance (1-2%). |
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| .. | ||
| apis | ||
| functions | ||
| ml-configuring-aggregations.asciidoc | ||
| ml-configuring-alerts.asciidoc | ||
| ml-configuring-categories.asciidoc | ||
| ml-configuring-detector-custom-rules.asciidoc | ||
| ml-configuring-populations.asciidoc | ||
| ml-configuring-transform.asciidoc | ||
| ml-configuring-url.asciidoc | ||
| ml-delayed-data-detection.asciidoc | ||