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Preview data frame analytics API

New API reference

For the most up-to-date API details, refer to Machine learning data frame analytics APIs.

Previews the features used by a data frame analytics config.

Request

GET _ml/data_frame/analytics/_preview

POST _ml/data_frame/analytics/_preview

GET _ml/data_frame/analytics/<data_frame_analytics_id>/_preview

POST _ml/data_frame/analytics/<data_frame_analytics_id>/_preview

Prerequisites

Requires the monitor_ml cluster privilege. This privilege is included in the machine_learning_user built-in role.

Description

This API provides preview of the extracted features for a data frame analytics config that either exists already or one that has not been created yet.

Path parameters

<data_frame_analytics_id>
(Optional, string) Identifier for the data frame analytics job.

Request body

config
(Optional, object) A data frame analytics config as described in Create data frame analytics jobs. Note that id and dest don’t need to be provided in the context of this API.

Response body

The API returns a response that contains the following:

feature_values
(array) An array of objects that contain feature name and value pairs. The features have been processed and indicate what will be sent to the model for training.

Examples

resp = client.ml.preview_data_frame_analytics(
    config={
        "source": {
            "index": "houses_sold_last_10_yrs"
        },
        "analysis": {
            "regression": {
                "dependent_variable": "price"
            }
        }
    },
)
print(resp)
const response = await client.ml.previewDataFrameAnalytics({
  config: {
    source: {
      index: "houses_sold_last_10_yrs",
    },
    analysis: {
      regression: {
        dependent_variable: "price",
      },
    },
  },
});
console.log(response);
POST _ml/data_frame/analytics/_preview
{
  "config": {
    "source": {
      "index": "houses_sold_last_10_yrs"
    },
    "analysis": {
      "regression": {
        "dependent_variable": "price"
      }
    }
  }
}

The API returns the following results:

{
  "feature_values": [
    {
      "number_of_bedrooms": "1",
      "postcode": "29655",
      "price": "140.4"
    },
    ...
  ]
}

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https://www.elastic.co/guide/en/elasticsearch/reference/8.17/preview-dfanalytics.html

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