[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. For example, it can contain <<binary-sc-resources>>.

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

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