[DOCS] Adds AUC ROC classification metric to the API examples (#63563) (#63596)

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István Zoltán Szabó 2020-10-13 12:18:29 +02:00 committed by GitHub
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1 changed files with 47 additions and 4 deletions

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@ -191,10 +191,10 @@ belongs.
positive.
`class_name`::::
(Required, string) Name of the only class that will be treated as
positive during AUC ROC calculation. Other classes will be treated as
negative ("one-vs-all" strategy). All the evaluated documents must have `class_name`
in the list of their top classes.
(Required, string) Name of the only class that is treated as positive
during AUC ROC calculation. Other classes are treated as negative
("one-vs-all" strategy). All the evaluated documents must have
`class_name` in the list of their top classes.
`include_curve`::::
(Optional, boolean) Whether or not the curve should be returned in
@ -496,3 +496,46 @@ predictions associated with the class.
<5> The number of cats in the dataset that are incorrectly classified as dogs.
<6> The number of documents that are classified as a class that is not listed as
a `predicted_class`.
[source,console]
--------------------------------------------------
POST _ml/data_frame/_evaluate
{
"index": "animal_classification",
"evaluation": {
"classification": { <1>
"actual_field": "animal_class", <2>
"metrics": {
"auc_roc" : { <3>
"class_name": "dog" <4>
}
}
}
}
}
--------------------------------------------------
// TEST[skip:TBD]
<1> The evaluation type.
<2> The field that contains the ground truth value for the actual animal
classification. This is required in order to evaluate results.
<3> Specifies the metric for the evaluation.
<4> Specifies the class name that is treated as positive during the evaluation,
all the other classes are treated as negative.
The API returns the following result:
[source,console-result]
--------------------------------------------------
{
"classification" : {
"auc_roc" : {
"score" : 0.8941788639536681
}
}
}
--------------------------------------------------
// TEST[skip:TBD]