anchor.diff:  end::detector-index[]
   
   tag::dfas-alpha[]
  -Advanced configuration option. {ml-cap} uses loss guided tree growing.
  -This means that trees will grow where the regularized loss reduces
  -the most. This parameter multiplies a term based on tree depth in
  -the regularized loss. Higher values result in shallower trees
  -and faster training times. Values should be greater than or equal
  -to zero. By default, this value is calculated during hyperparameter optimization.
  +Advanced configuration option. {ml-cap} uses loss guided tree growing, which
  +means that the decision trees grow where the regularized loss decreases most
  +quickly. This parameter affects loss calculations by acting as a multiplier of 
  +the tree depth. Higher alpha values result in shallower trees and faster 
  +training times. By default, this value is calculated during hyperparameter 
  +optimization. It must be greater than or equal to zero. 
   end::dfas-alpha[]
   
   tag::dfas-downsample-factor[]
  -Advanced configuration option. This controls the fraction of data
  -that is used to compute the derivatives of the loss function for tree training.
  -The lower the value the smaller the fraction of data that is used.
  -Typically accuracy improves if this is set to be less than 1. However, too small
  -a value may result in poor convergence for the ensemble and so require more trees.
  -For more information about shrinkage, refer to
  +Advanced configuration option. Controls the fraction of data that is used to 
  +compute the derivatives of the loss function for tree training. A small value 
  +results in the use of a small fraction of the data. If this value is set to be 
  +less than 1, accuracy typically improves. However, too small a value may result 
  +in poor convergence for the ensemble and so require more trees. For more
  +information about shrinkage, refer to
   {wikipedia}/Gradient_boosting#Stochastic_gradient_boosting[this wiki article].
  -Values must be greater than zero and less than or equal to 1.
  -By default, this value is calculated during hyperparameter optimization.
  +By default, this value is calculated during hyperparameter optimization. It 
  +must be greater than zero and less than or equal to 1.
   end::dfas-downsample-factor[]
   
   tag::dfas-early-stopping-enabled[]
  
positive.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, the smaller the value the longer the training will take. For more
  information about shrinkage, refer to
  {wikipedia}/Gradient_boosting#Shrinkage[this wiki article].
  By default, this value is calculated during hyperparameter optimization.
  end::eta[]
  
  tag::exclude-frequent[]
  
negative.code: end::max-empty-searches[]
  
  tag::max-trees[]
  Advanced configuration option. Defines the maximum number of decision trees in 
  the forest. The maximum value is 2000. By default, this value is calculated 
  during hyperparameter optimization.
  end::max-trees[]
  
  tag::method[]
  