bestEATBoost: Tuning an EATBoost model

In boostingDEA: A Boosting Approach to Data Envelopment Analysis

Tuning an EATBoost model

Description

This function computes the root mean squared error (RMSE) for a set of EATBoost models built with a grid of given hyperparameters.

Usage

bestEATBoost(
  training,
  test,
  x,
  y,
  num.iterations,
  learning.rate,
  num.leaves,
  verbose = TRUE
)

Arguments

training	Training data.frame or matrix containing the variables for model construction.
test	Test data.frame or matrix containing the variables for model assessment.
x	Column input indexes in training.
y	Column output indexes in training.
num.iterations	Maximum number of iterations the algorithm will perform
learning.rate	Learning rate that control overfitting of the algorithm. Value must be in (0,1]
num.leaves	Maximum number of terminal leaves in each tree at each iteration
verbose	Controls the verbosity.

Value

A data.frame with the sets of hyperparameters and the root mean squared error (RMSE) and mean square error (MSE) associated for each model.

boostingDEA documentation built on May 31, 2023, 6:33 p.m.