OpenSearch/docs/reference/ml/df-analytics/apis/evaluateresources.asciidoc

[role="xpack"]
[testenv="platinum"]
[[ml-evaluate-dfanalytics-resources]]
=== {dfanalytics-cap} evaluation resources

Evaluation configuration objects relate to the <<evaluate-dfanalytics>>.

[discrete]
[[ml-evaluate-dfanalytics-properties]]
==== {api-definitions-title}

`evaluation`::
  (object) Defines the type of evaluation you want to perform. The value of this 
  object can be different depending on the type of evaluation you want to 
  perform.
+
--
Available evaluation types:
* `binary_soft_classification`
* `regression`
* `classification`
--
  
`query`::
  (object) A query clause that retrieves a subset of data from the source index. 
  See <<query-dsl>>. The evaluation only applies to those documents of the index 
  that match the query.


[[binary-sc-resources]]
==== Binary soft classification configuration objects

Binary soft classification evaluates the results of an analysis which outputs 
the probability that each document belongs to a certain class. For 
example, in the context of outlier detection, the analysis outputs the 
probability whether each document is an outlier.

[discrete]
[[binary-sc-resources-properties]]
===== {api-definitions-title}

`actual_field`::
  (string) The field of the `index` which contains the `ground truth`. 
  The data type of this field can be boolean or integer. If the data type is 
  integer, the value has to be either `0` (false) or `1` (true).

`predicted_probability_field`::
  (string) The field of the `index` that defines the probability of 
  whether the item belongs to the class in question or not. It's the field that 
  contains the results of the analysis.

`metrics`::
  (object) Specifies the metrics that are used for the evaluation. 
  Available metrics:
  
  `auc_roc`::
    (object) The AUC ROC (area under the curve of the receiver operating 
    characteristic) score and optionally the curve.
    Default value is {"includes_curve": false}.
    
  `precision`::
    (object) Set the different thresholds of the {olscore} at where the metric 
    is calculated.
    Default value is {"at": [0.25, 0.50, 0.75]}.
  
  `recall`::
    (object) Set the different thresholds of the {olscore} at where the metric 
    is calculated.
    Default value is {"at": [0.25, 0.50, 0.75]}.
  
  `confusion_matrix`::
    (object) Set the different thresholds of the {olscore} at where the metrics 
    (`tp` - true positive, `fp` - false positive, `tn` - true negative, `fn` - 
    false negative) are calculated.
    Default value is {"at": [0.25, 0.50, 0.75]}.

    
[[regression-evaluation-resources]]
==== {regression-cap} evaluation objects

{regression-cap} evaluation evaluates the results of a {regression} analysis 
which outputs a prediction of values.


[discrete]
[[regression-evaluation-resources-properties]]
===== {api-definitions-title}

`actual_field`::
  (string) The field of the `index` which contains the `ground truth`. The data 
  type of this field must be numerical.
  
`predicted_field`::
  (string) The field in the `index` that contains the predicted value, 
  in other words the results of the {regression} analysis.
  
`metrics`::
  (object) Specifies the metrics that are used for the evaluation. Available 
  metrics are `r_squared` and `mean_squared_error`.
  
  
[[classification-evaluation-resources]]
==== {classification-cap} evaluation objects

{classification-cap} evaluation evaluates the results of a {classanalysis} which 
outputs a prediction that identifies to which of the classes each document 
belongs.


[discrete]
[[classification-evaluation-resources-properties]]
===== {api-definitions-title}

`actual_field`::
  (string) The field of the `index` which contains the ground truth. The data 
  type of this field must be keyword.
  
`metrics`::
  (object) Specifies the metrics that are used for the evaluation. Available 
  metric is `multiclass_confusion_matrix`.
  
`predicted_field`::
  (string) The field in the `index` that contains the predicted value, in other 
  words the results of the {classanalysis}. The data type of this field is 
  string. You need to add `.keyword` to the predicted field name (the name you 
  put in the {classanalysis} object as `prediction_field_name` or the default 
  value of the same field if you didn't specified explicitly). For example, 
  `predicted_field` : `ml.animal_class_prediction.keyword`.