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authorDaniel Baumann <daniel.baumann@progress-linux.org>2022-11-30 18:47:05 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2022-11-30 18:47:05 +0000
commit97e01009d69b8fbebfebf68f51e3d126d0ed43fc (patch)
tree02e8b836c3a9d89806f3e67d4a5fe9f52dbb0061 /ml/README.md
parentReleasing debian version 1.36.1-1. (diff)
downloadnetdata-97e01009d69b8fbebfebf68f51e3d126d0ed43fc.tar.xz
netdata-97e01009d69b8fbebfebf68f51e3d126d0ed43fc.zip
Merging upstream version 1.37.0.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
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@@ -248,8 +248,6 @@ In terms of anomaly detection, the most interesting charts would be the `anomaly
- `anomaly_detection.dimensions`: Percentage of anomalous dimensions.
- `anomaly_detection.detector_window`: The length of the active window used by the detector.
- `anomaly_detection.detector_events`: Flags (0 or 1) to show when an anomaly event has been triggered by the detector.
-- `anomaly_detection.prediction_stats`: Diagnostic metrics relating to prediction time of anomaly detection.
-- `anomaly_detection.training_stats`: Diagnostic metrics relating to training time of anomaly detection.
Below is an example of how these charts may look in the presence of an anomaly event.