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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2022-11-30 18:47:05 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2022-11-30 18:47:05 +0000 |
commit | 97e01009d69b8fbebfebf68f51e3d126d0ed43fc (patch) | |
tree | 02e8b836c3a9d89806f3e67d4a5fe9f52dbb0061 /ml/README.md | |
parent | Releasing debian version 1.36.1-1. (diff) | |
download | netdata-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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-rw-r--r-- | ml/README.md | 2 |
1 files changed, 0 insertions, 2 deletions
diff --git a/ml/README.md b/ml/README.md index 2578993e2..f6fd923ab 100644 --- a/ml/README.md +++ b/ml/README.md @@ -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. |