bestEAT {eat} | R Documentation |
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 |
test |
Test |
x |
Column input indexes in |
y |
Column output indexes in |
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 |
|
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))
[Package eat version 0.1.4 Index]