@cryptotaxi247 / netdata-1 / commits / c53d2fc22

update ml defaults in docs (#12782)

Andrew Maguire committed May 3, 2022 at 18:59 UTC c53d2fc22538018f662494f9f08371d69c83bbab

1 file changed +2 -2

ml/README.md

+2 -2

@@ -160,7 +160,7 @@ Below is a list of all the available configuration params and their default valu

160 # maximum num samples to train = 14400

161 # minimum num samples to train = 3600

162 # train every = 3600

163 - # dbengine anomaly rate every = 60

163 + # dbengine anomaly rate every = 30

164 # num samples to diff = 1

165 # num samples to smooth = 3

166 # num samples to lag = 5

@@ -225,7 +225,7 @@ This example assumes 3 child nodes [streaming](https://learn.netdata.cloud/docs/

225 ### Descriptions (min/max)

226

227 - `enabled`: `yes` to enable, `no` to disable.

228 -- `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.

228 +- `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.

229 - `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.

230 - `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.

231 - `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.