elasticsearch/docs/reference/ml/apis/evaluate-dfanalytics.asciidoc

[role="xpack"]
[testenv="platinum"]
[[evaluate-dfanalytics]]
=== Evaluate {dfanalytics} API

[subs="attributes"]
++++
<titleabbrev>Evaluate {dfanalytics}</titleabbrev>
++++

experimental[]

Evaluates the executed analysis on an index that is already annotated with a 
field that contains the results of the analytics (the `ground truth`) for each 
{dataframe} row. Evaluation is typically done via calculating a set of metrics 
that capture various aspects of the quality of the results over the data for 
which we have the `ground truth`. For different types of analyses different 
metrics are suitable. This API packages together commonly used metrics for 
various analyses.

[[ml-evaluate-dfanalytics-request]]
==== {api-request-title}

`POST _ml/data_frame/_evaluate`

[[ml-evaluate-dfanalytics-prereq]]
==== {api-prereq-title}

* You must have `monitor_ml` privilege to use this API. For more 
information, see {stack-ov}/security-privileges.html[Security privileges] and 
{stack-ov}/built-in-roles.html[Built-in roles].

[[ml-evaluate-dfanalytics-request-body]]
==== {api-request-body-title}

`index` (Required)::
  (object) Defines the `index` in which the evaluation will be performed.
  
`evaluation` (Required)::
  (object) Defines the type of evaluation you want to perform. For example: 
  `binary_soft_classification`.
  See Evaluate API resources.

[[ml-evaluate-dfanalytics-example]]
==== {api-examples-title}

[source,js]
--------------------------------------------------
POST _ml/data_frame/_evaluate
{
  "index": "my_analytics_dest_index",
  "evaluation": {
    "binary_soft_classification": {
      "actual_field": "is_outlier",
      "predicted_probability_field": "ml.outlier_score"
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[skip:TBD]

The API returns the following results:

[source,js]
----
{
  "binary_soft_classification": {
    "auc_roc": {
      "score": 0.92584757746414444
    },
    "confusion_matrix": {
      "0.25": {
          "tp": 5,
          "fp": 9,
          "tn": 204,
          "fn": 5
      },
      "0.5": {
          "tp": 1,
          "fp": 5,
          "tn": 208,
          "fn": 9
      },
      "0.75": {
          "tp": 0,
          "fp": 4,
          "tn": 209,
          "fn": 10
      }
    },
    "precision": {
        "0.25": 0.35714285714285715,
        "0.5": 0.16666666666666666,
        "0.75": 0
    },
    "recall": {
        "0.25": 0.5,
        "0.5": 0.1,
        "0.75": 0
    }
  }
}
----
// TESTRESPONSE