bestEATBoost {boostingDEA}

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.