anchor.diff:    in other words the results of the {regression} analysis.
     
   `metrics`::
  -  (Optional, object) Specifies the metrics that are used for the evaluation.
  +  (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].
  +    (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.
  +    (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].
  +    (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].
  +    (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].
   
   
     
  
positive.code: 
  * cluster: `monitor_ml`
    
  For more information, see <<security-privileges>> and {ml-docs-setup-privileges}.
  
  
  [[ml-evaluate-dfanalytics-desc]]
  
negative.code: [[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.
  
  `actual_field`::
    (Required, string) The field of the `index` which contains the `ground truth`. 
  