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Elastic Docs ›Elasticsearch Guide [master] ›REST APIs ›Machine learning data frame analytics APIs

Preview data frame analytics API

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Preview data frame analytics API

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Previews the features used by a data frame analytics config.

Request

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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

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

Description

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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

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

Request body

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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

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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

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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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