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| author | Andrew Maguire <andrewm4894@gmail.com> | 2022-05-03 18:59:07 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-05-03 20:59:07 +0300 |
| commit | c53d2fc22538018f662494f9f08371d69c83bbab (patch) | |
| tree | 9bc7ec0b9ac35192162d02bdd30d95e9e620c909 /ml | |
| parent | ebdd819d6ead44c5bab80c69be4a7d419402fe3c (diff) | |
update ml defaults in docs (#12782)
Diffstat (limited to 'ml')
| -rw-r--r-- | ml/README.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/ml/README.md b/ml/README.md index 95d9fe6abd..cb8384a66b 100644 --- a/ml/README.md +++ b/ml/README.md @@ -160,7 +160,7 @@ Below is a list of all the available configuration params and their default valu # maximum num samples to train = 14400 # minimum num samples to train = 3600 # train every = 3600 - # dbengine anomaly rate every = 60 + # dbengine anomaly rate every = 30 # num samples to diff = 1 # num samples to smooth = 3 # num samples to lag = 5 @@ -225,7 +225,7 @@ This example assumes 3 child nodes [streaming](https://learn.netdata.cloud/docs/ ### Descriptions (min/max) - `enabled`: `yes` to enable, `no` to disable. -- `maximum num samples to train`: (`3600`/`21600`) This is the maximum amount of time you would like to train each model on. For example, the default of `14400` trains on the preceding 4 hours of data, assuming an `update every` of 1 second. +- `maximum num samples to train`: (`3600`/`86400`) This is the maximum amount of time you would like to train each model on. For example, the default of `14400` trains on the preceding 4 hours of data, assuming an `update every` of 1 second. - `minimum num samples to train`: (`900`/`21600`) This is the minimum amount of data required to be able to train a model. For example, the default of `3600` implies that once at least 1 hour of data is available for training, a model is trained, otherwise it is skipped and checked again at the next training run. - `train every`: (`1800`/`21600`) This is how often each model will be retrained. For example, the default of `3600` means that each model is retrained every hour. Note: The training of all models is spread out across the `train every` period for efficiency, so in reality, it means that each model will be trained in a staggered manner within each `train every` period. - `dbengine anomaly rate every`: (`30`/`900`) This is how often netdata will aggregate all the anomaly bits into a single chart (`anomaly_detection.anomaly_rates`). The aggregation into a single chart allows enabling anomaly rate ranking over _all_ metrics with one API call as opposed to a call per chart. |