OpenSearch/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

experimental::[]

Evaluates the {dfanalytics} for an annotated index.
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> Evaluation to be performed

==== Evaluation

Evaluation to be performed.
Currently, supported evaluations include: +OutlierDetection+, +Classification+, +Regression+.

===== Outlier detection

["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests-file}[{api}-evaluation-outlierdetection]
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<1> Constructing a new evaluation
<2> 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.
<3> Name of the field in the index. Its value denotes the probability (as per some ML algorithm) of the example being classified as positive.
<4> The remaining parameters are the metrics to be calculated based on the two fields described above
<5> {wikipedia}/Precision_and_recall#Precision[Precision] calculated at thresholds: 0.4, 0.5 and 0.6
<6> {wikipedia}/Precision_and_recall#Recall[Recall] calculated at thresholds: 0.5 and 0.7
<7> {wikipedia}/Confusion_matrix[Confusion matrix] calculated at threshold 0.5
<8> {wikipedia}/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated and the curve points returned

===== Classification

["source","java",subs="attributes,callouts,macros"]
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include-tagged::{doc-tests-file}[{api}-evaluation-classification]
--------------------------------------------------
<1> Constructing a new evaluation
<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) class the example belongs to.
<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) class of the example.
<4> Name of the field in the index. Its value denotes the array of top classes. Must be nested.
<5> The remaining parameters are the metrics to be calculated based on the two fields described above
<6> Accuracy
<7> Precision
<8> Recall
<9> Multiclass confusion matrix of size 3
<10> {wikipedia}/Receiver_operating_characteristic#Area_under_the_curve[AuC ROC] calculated for class "cat" treated as positive and the rest as negative

===== Regression

["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests-file}[{api}-evaluation-regression]
--------------------------------------------------
<1> Constructing a new evaluation
<2> Name of the field in the index. Its value denotes the actual (i.e. ground truth) value for an example.
<3> Name of the field in the index. Its value denotes the predicted (as per some ML algorithm) value for the example.
<4> The remaining parameters are the metrics to be calculated based on the two fields described above
<5> {wikipedia}/Mean_squared_error[Mean squared error]
<6> Mean squared logarithmic error
<7> {wikipedia}/Huber_loss#Pseudo-Huber_loss_function[Pseudo Huber loss]
<8> {wikipedia}/Coefficient_of_determination[R squared]

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

==== Results

===== Outlier detection

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

===== Classification

["source","java",subs="attributes,callouts,macros"]
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include-tagged::{doc-tests-file}[{api}-results-classification]
--------------------------------------------------

<1> Fetching accuracy metric by name
<2> Fetching the actual accuracy value
<3> Fetching precision metric by name
<4> Fetching the actual precision value
<5> Fetching recall metric by name
<6> Fetching the actual recall value
<7> Fetching multiclass confusion matrix metric by name
<8> Fetching the contents of the confusion matrix
<9> Fetching the number of classes that were not included in the matrix
<10> Fetching AucRoc metric by name
<11> Fetching the actual AucRoc score
<12> Fetching the number of documents that were used in order to calculate AucRoc score

===== Regression

["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests-file}[{api}-results-regression]
--------------------------------------------------

<1> Fetching mean squared error metric by name
<2> Fetching the actual mean squared error value
<3> Fetching mean squared logarithmic error metric by name
<4> Fetching the actual mean squared logarithmic error value
<5> Fetching pseudo Huber loss metric by name
<6> Fetching the actual pseudo Huber loss value
<7> Fetching R squared metric by name
<8> Fetching the actual R squared value