boostingDEA (version 0.1.0)

EATBoost: Gradient Tree Boosting

Description

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

Usage

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
)

Value

A EATBoost object.

A EATBoost object.

Arguments

data

data.frame or matrix containing the variables in the model.

x

Column input indexes in data.

y

Column output indexes in data.

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