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This PR adds the resource documentation of the data frame analytics APIs and the evaluate API to the ML API doc pool.
126 lines
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2.9 KiB
Text
126 lines
No EOL
2.9 KiB
Text
[role="xpack"]
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[testenv="platinum"]
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[[evaluate-dfanalytics]]
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=== Evaluate {dfanalytics} API
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[subs="attributes"]
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++++
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<titleabbrev>Evaluate {dfanalytics}</titleabbrev>
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++++
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Evaluates the {dfanalytics} for an annotated index.
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experimental[]
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[[ml-evaluate-dfanalytics-request]]
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==== {api-request-title}
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`POST _ml/data_frame/_evaluate`
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[[ml-evaluate-dfanalytics-prereq]]
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==== {api-prereq-title}
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* You must have `monitor_ml` privilege to use this API. For more
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information, see {stack-ov}/security-privileges.html[Security privileges] and
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{stack-ov}/built-in-roles.html[Built-in roles].
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[[ml-evaluate-dfanalytics-desc]]
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==== {api-description-title}
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This API evaluates the executed analysis on an index that is already annotated
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with a field that contains the results of the analytics (the `ground truth`)
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for each {dataframe} row.
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Evaluation is typically done by calculating a set of metrics that capture various aspects of the quality of the results over the data for which you have the
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`ground truth`.
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For different types of analyses different metrics are suitable. This API
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packages together commonly used metrics for various analyses.
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[[ml-evaluate-dfanalytics-request-body]]
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==== {api-request-body-title}
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`index` (Required)::
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(object) Defines the `index` in which the evaluation will be performed.
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`evaluation` (Required)::
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(object) Defines the type of evaluation you want to perform. For example:
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`binary_soft_classification`. See <<ml-evaluate-dfanalytics-resources>>.
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[[ml-evaluate-dfanalytics-results]]
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==== {api-response-body-title}
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`binary_soft_classification`::
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(object) If you chose to do binary soft classification, the API returns the
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following evaluation metrics:
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`auc_roc`::: TBD
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`confusion_matrix`::: TBD
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`precision`::: TBD
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`recall`::: TBD
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[[ml-evaluate-dfanalytics-example]]
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==== {api-examples-title}
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[source,js]
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--------------------------------------------------
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POST _ml/data_frame/_evaluate
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{
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"index": "my_analytics_dest_index",
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"evaluation": {
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"binary_soft_classification": {
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"actual_field": "is_outlier",
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"predicted_probability_field": "ml.outlier_score"
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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// TEST[skip:TBD]
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The API returns the following results:
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[source,js]
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----
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{
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"binary_soft_classification": {
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"auc_roc": {
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"score": 0.92584757746414444
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},
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"confusion_matrix": {
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"0.25": {
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"tp": 5,
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"fp": 9,
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"tn": 204,
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"fn": 5
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},
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"0.5": {
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"tp": 1,
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"fp": 5,
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"tn": 208,
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"fn": 9
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},
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"0.75": {
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"tp": 0,
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"fp": 4,
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"tn": 209,
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"fn": 10
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}
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},
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"precision": {
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"0.25": 0.35714285714285715,
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"0.5": 0.16666666666666666,
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"0.75": 0
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},
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"recall": {
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"0.25": 0.5,
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"0.5": 0.1,
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"0.75": 0
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}
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}
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}
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----
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// TESTRESPONSE |