elasticsearch/docs/java-rest/high-level/ml/evaluate-data-frame.asciidoc

--
:api: evaluate-data-frame
:request: EvaluateDataFrameRequest
:response: EvaluateDataFrameResponse
--
[role="xpack"]
[id="{upid}-{api}"]
=== Evaluate {dfanalytics} API

Evaluates the {ml} algorithm that ran on a {dataframe}.
The API accepts an +{request}+ object and returns an +{response}+.

[id="{upid}-{api}-request"]
==== Evaluate {dfanalytics} request

["source","java",subs="attributes,callouts,macros"]
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include-tagged::{doc-tests-file}[{api}-request]
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<1> Constructing a new evaluation request
<2> Reference to an existing index
<3> The query with which to select data from indices
<4> Kind of evaluation to perform
<5> Name of the field in the index. Its value denotes the actual (i.e. ground truth) label for an example. Must be either true or false
<6> Name of the field in the index. Its value denotes the probability (as per some ML algorithm) of the example being classified as positive
<7> The remaining parameters are the metrics to be calculated based on the two fields described above.
<8> https://en.wikipedia.org/wiki/Precision_and_recall[Precision] calculated at thresholds: 0.4, 0.5 and 0.6
<9> https://en.wikipedia.org/wiki/Precision_and_recall[Recall] calculated at thresholds: 0.5 and 0.7
<10> https://en.wikipedia.org/wiki/Confusion_matrix[Confusion matrix] calculated at threshold 0.5
<11> https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated and the curve points returned

include::../execution.asciidoc[]

[id="{upid}-{api}-response"]
==== Response

The returned +{response}+ contains the requested evaluation metrics.

["source","java",subs="attributes,callouts,macros"]
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include-tagged::{doc-tests-file}[{api}-response]
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<1> Fetching all the calculated metrics results
<2> Fetching precision metric by name
<3> Fetching precision at a given (0.4) threshold
<4> Fetching confusion matrix metric by name
<5> Fetching confusion matrix at a given (0.5) threshold