anchor.diff:  tag::eta[]
   Advanced configuration option. The shrinkage applied to the weights. Smaller
   values result in larger forests which have a better generalization error.
  -However, the smaller the value the longer the training will take. For more
  -information about shrinkage, refer to
  +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.
  +By default, this value is calculated during hyperparameter optimization. It must
  +be a value between 0.001 and 1.
   end::eta[]
   
   tag::exclude-frequent[]
  
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::lambda[]
  Advanced configuration option. Regularization parameter to prevent overfitting
  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}. 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[]
  