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Signed-off-by: Naarcha-AWS <naarcha@amazon.com>
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Naarcha-AWS 2022-05-26 13:35:56 -05:00
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10
_ml-commons-plugin/algorithms.md
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@ -57,9 +57,9 @@ POST /_plugins/_ml/_train/kmeans
The training process supports multi-threads, but the number of threads should be less than half of the number of CPUs.
## Linear Regression
## Linear regression
Linear Regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](http://chrome-extension//gphandlahdpffmccakmbngmbjnjiiahp/https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_DROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html).
Linear regression maps the linear relationship between inputs and outputs. In ML Commons, the linear regression algorithm is adopted from the public machine learning library [Tribuo](https://tribuo.org/), which offers multidimensional linear regression models. The model supports the linear optimizer in training, including popular approaches like Linear Decay, SQRT_DECAY, [ADA](http://chrome-extension//gphandlahdpffmccakmbngmbjnjiiahp/https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf), [ADAM](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/Adam.html), and [RMS_DROP](https://tribuo.org/learn/4.1/javadoc/org/tribuo/math/optimisers/RMSProp.html).
### Parameters
@ -172,6 +172,8 @@ anomaly_score_threshold | double | The threshold of the anomaly score | 1.0
#### Fit RCF
All parameters are optional except `time_field`.
Parameter | Type | Description | Default Value
:--- |:--- | :--- | :---
number_of_trees | integer | The number of trees in the forest | 30
@ -202,6 +204,8 @@ The Anomaly Localization algorithm finds subset level-information for aggregate
### Parameters
All parameters are required except `filter_query` and `anomaly_start`.
Parameter | Type | Description | Default Value
:--- | :--- | :--- | :---
index_name | String | The data collection to analyze | N/A
@ -217,7 +221,7 @@ anomaly_star | QueryBuilder | (Optional) The time after which the data will be a
### Example
The following example executes Anomaly Localization against an RCA index. The API responds with 10 aggregations and gives the sum of the contribution and base values per aggregation, every
The following example executes Anomaly Localization against an RCA index.
**Request**

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_ml-commons-plugin/api.md
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@ -232,7 +232,7 @@ The API returns the following:
## Predict
ML Commons can predict new data with your trained model either from indexed data or a data frame. To use the Predict API, the model_id is required.
ML Commons can predict new data with your trained model either from indexed data or a data frame. To use the Predict API, the `model_id` is required.
```json
POST /_plugins/_ml/_predict/<algorithm_name>/<model_id>