mlr3superlearner {mlr3superlearner} | R Documentation |
Super Learner Algorithm
Description
Implementation of the Super Learner algorithm using the 'mlr3' framework. By default, returning the discrete Super Learner. If using the ensemble Super Learner, The LASSO with an alpha value of 0 and a restriction on the lower limit of the coefficients is used as the meta-learner.
Usage
mlr3superlearner(
data,
target,
library,
outcome_type = c("binomial", "continuous"),
folds = NULL,
discrete = TRUE,
newdata = NULL,
group = NULL,
info = FALSE
)
Arguments
data |
[ |
target |
[ |
library |
[ |
outcome_type |
[ |
folds |
[ |
discrete |
[ |
newdata |
[ |
group |
[ |
info |
[ |
Value
A list of class mlr3superlearner
.
Examples
if (requireNamespace("ranger", quietly = TRUE)) {
n <- 1e3
W <- matrix(rnorm(n*3), ncol = 3)
A <- rbinom(n, 1, 1 / (1 + exp(-(.2*W[,1] - .1*W[,2] + .4*W[,3]))))
Y <- rbinom(n,1, plogis(A + 0.2*W[,1] + 0.1*W[,2] + 0.2*W[,3]^2 ))
tmp <- data.frame(W, A, Y)
mlr3superlearner(tmp, "Y", c("glm", "ranger"), "binomial")
}