anchor.diff:  ==== {api-request-body-title}
   
   `evaluation`::
  -(Required, 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. See <<ml-evaluate-dfanalytics-resources>>.
  +(Required, object) Defines the type of evaluation you want to perform.
  +See <<ml-evaluate-dfanalytics-resources>>.
   +
   --
   Available evaluation types:
  +
   * `binary_soft_classification`
   * `regression`
   * `classification`
  +
   --
   
   `index`::
  
positive.code:   in other words the results of the {regression} analysis.
    
  `metrics`::
    (Required, 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
  
negative.code: performed.
  
  `query`::
  (Optional, object) A query clause that retrieves a subset of data from the
  source index. See <<query-dsl>>.
  
  [[ml-evaluate-dfanalytics-resources]]
  ==== {dfanalytics-cap} evaluation resources
  
  [[binary-sc-resources]]
  ===== Binary soft classification evaluation 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 
  