EATBoost {boostingDEA}  R Documentation 
This function estimates a production frontier satisfying some classical production theory axioms, such as monotonicity and determinictiness, which is based upon the adaptation of the machine learning technique known as Gradient Tree Boosting
This function saves information about the EATBoost model
EATBoost(data, x, y, num.iterations, num.leaves, learning.rate)
EATBoost_object(
data,
x,
y,
num.iterations,
num.leaves,
learning.rate,
EAT.models,
f0,
prediction
)
data 

x 
Column input indexes in 
y 
Column output indexes in 
num.iterations 
Maximum number of iterations the algorithm will perform 
num.leaves 
Maximum number of terminal leaves in each tree at each iteration. 
learning.rate 
Learning rate that control overfitting of the algorithm. Value must be in (0,1] 
EAT.models 
List of the EAT models created in each iterations 
f0 
Initial predictions of the model (they correspond to maximum value of each output variable) 
prediction 
Final predictions of the original data 
A EATBoost
object.
A EATBoost
object.