bestEAT: Tuning an Efficiency Analysis Trees model

In eat: Efficiency Analysis Trees

Tuning an Efficiency Analysis Trees model

Description

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

Usage

bestEAT(
  training,
  test,
  x,
  y,
  numStop = 5,
  fold = 5,
  max.depth = NULL,
  max.leaves = NULL,
  na.rm = 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.
numStop	Minimum number of observations in a node for a split to be attempted.
fold	Folds in which the dataset to apply cross-validation during the pruning is divided.
max.depth	Maximum depth of the tree.
max.leaves	Maximum number of leaf nodes.
na.rm	logical. If TRUE, NA rows are omitted.

Value

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

Examples

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

bestEAT(training = training, 
        test = test,
        x = 6:9,
        y = 3,
        numStop = c(3, 5, 7),
        fold = c(5, 7, 10))

eat documentation built on Jan. 10, 2023, 5:13 p.m.