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

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[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