eat (version 0.1.4)

bestRFEAT: Tuning a Random Forest + Efficiency Analysis Trees model

Description

This funcion computes the root mean squared error (RMSE) for a set of Random FOrest + Efficiency Analysis Trees models built with a grid of given hyperparameters.

Usage

bestRFEAT(
  training,
  test,
  x,
  y,
  numStop = 5,
  m = 50,
  s_mtry = c("5", "BRM"),
  na.rm = TRUE
)

Value

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

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.

numStop

Minimum number of observations in a node for a split to be attempted.

m

Number of trees to be built.

s_mtry

character. Number of inputs to be selected in each split. See ``

na.rm

logical. If TRUE, NA rows are omitted.

Examples

# \donttest{
data("PISAindex")

n <- nrow(PISAindex) # Observations in the dataset
selected <- sample(1:n, n * 0.7) # Training indexes
training <- PISAindex[selected, ] # Training set
test <- PISAindex[- selected, ] # Test set

bestRFEAT(training = training, 
          test = test,
          x = 6:9,
          y = 3,
          numStop = c(3, 5),
          m = c(20, 30),
          s_mtry = c("1", "BRM"))
# }