anchor.diff:  
   tag::lambda[]
   Advanced configuration option. Regularization parameter to prevent overfitting
  -on the training data set. Multiplies an L2 regularisation term which applies to
  -leaf weights of the individual trees in the forest. The higher the value the
  -more training will attempt to keep leaf weights small. This makes the prediction
  +on the training data set. Multiplies an L2 regularization term which applies to
  +leaf weights of the individual trees in the forest. A high lambda value causes 
  +training to favor small leaf weights. This behavior makes the prediction 
   function smoother at the expense of potentially not being able to capture
  -relevant relationships between the features and the {depvar}. The smaller this
  -parameter the larger individual trees will be and the longer training will take.
  -By default, this value is calculated during hyperparameter optimization.
  +relevant relationships between the features and the {depvar}. A small lambda
  +value results in large individual trees and slower training. By default, this 
  +value is calculated during hyperparameter optimization. It must be a nonnegative
  +value.
   end::lambda[]
   
   tag::last-data-time[]
  
positive.code: end::dfas-early-stopping-enabled[]
  
  tag::dfas-eta-growth[]
  Advanced configuration option.
  Specifies the rate at which `eta` increases for each new tree that is added
  to the forest. For example, a rate of `1.05` increases `eta` by 5% for each
  extra tree. Values must be in the range of 0.5 to 2.
  By default, this value is calculated during hyperparameter optimization.
  end::dfas-eta-growth[]
  
  tag::dfas-feature-bag-fraction[]
  
negative.code: tag::eta[]
  Advanced configuration option. The shrinkage applied to the weights. Smaller
  values result in larger forests which have a better generalization error.
  However, larger forests cause slower training. For more information about 
  shrinkage, refer to
  {wikipedia}/Gradient_boosting#Shrinkage[this wiki article].
  By default, this value is calculated during hyperparameter optimization. It must
  be a value between 0.001 and 1.
  end::eta[]
  
  tag::exclude-frequent[]
  