anchor.diff:  [[oldetection-resources]]
   === {oldetection-cap} evaluation objects
   
  -{oldetection-cap} evaluates the results of an {oldetection} analysis which outputs
  -the probability that each document is an outlier.
  +{oldetection-cap} evaluates the results of an {oldetection} analysis which 
  +outputs the probability that each document is an outlier.
   
   `actual_field`::
     (Required, string) The field of the `index` which contains the `ground truth`. 
  
positive.code:   `auc_roc`:::
      (Optional, object) The AUC ROC (area under the curve of the receiver
      operating characteristic) score and optionally the curve.
      It is calculated for a specific class (provided as "class_name")
      treated as 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). Documents which do not have `class_name`
        in the list of their top classes will not be taken into account for evaluation.
        The number of documents taken into account is returned in the evaluation result
        (`auc_roc.doc_count` field).
  
      `include_curve`::::
        (Optional, boolean) Whether or not the curve should be returned in
  
negative.code:   in other words the results of the {regression} analysis.
    
  `metrics`::
    (Optional, object) Specifies the metrics that are used for the evaluation. For 
    more information on `mse`, `msle`, and `huber`, consult 
    https://github.com/elastic/examples/tree/master/Machine%20Learning/Regression%20Loss%20Functions[the Jupyter notebook on regression loss functions].
    Available metrics:
  
    `mse`:::
      (Optional, object) Average squared difference between the predicted values 
      and the actual (`ground truth`) value. For more information, read 
      {wikipedia}/Mean_squared_error[this wiki article].
  
    `msle`:::
      (Optional, object) Average squared difference between the logarithm of the 
      predicted values and the logarithm of the actual (`ground truth`) value.
      
      `offset`::::
        (Optional, double) Defines the transition point at which you switch from 
        minimizing quadratic error to minimizing quadratic log error. Defaults to 
        `1`.
  
    `huber`:::
      (Optional, object) Pseudo Huber loss function. For more information, read 
      {wikipedia}/Huber_loss#Pseudo-Huber_loss_function[this wiki article].
      
      `delta`::::
        (Optional, double) Approximates 1/2 (prediction - actual)^2^ for values 
        much less than delta and approximates a straight line with slope delta for 
        values much larger than delta. Defaults to `1`. Delta needs to be greater 
        than `0`.
  
    `r_squared`:::
      (Optional, object) Proportion of the variance in the dependent variable that 
      is predictable from the independent variables. For more information, read 
      {wikipedia}/Coefficient_of_determination[this wiki article].
  
  
    
  