Learners Available for Use
Description
Learners Available for Use
Usage
available_learners(outcome_type = c("binomial", "continuous"))
Arguments
outcome_type
The outcome variable type.
Value
A data.table of available learners.
Examples
available_learners("binomial")
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
[data.frame]
A data.frame containing predictors and target variable.
target
[character(1)]
The name of the target variable in data.
library
[character] or [list]
A vector or list of algorithms to be used for prediction.
outcome_type
[character(1)]
The outcome variable type. Options are "binomial" and "continuous".
folds
[numeric(1)]
The number of cross-validation folds, or if NULL will be dynamically determined.
discrete
[logical(1)]
Return the discrete Super Learner, or the ensemble Super Learner?
newdata
[list]
A list of data.frames to generate predictions from.
group
[character(1)]
Name of a grouping variable in data. Assumed to be discrete;
observations in the same group are treated like a "block" of observations
kept together during sample splitting.
info
[logical(1)]
Print learner fitting information to the console.
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")
}
Predict method for mlr3superlearner object
Description
Predict method for mlr3superlearner object
Usage
## S3 method for class 'mlr3superlearner'
predict(object, newdata, ...)
Arguments
object
[mlr3superlearner]
An object returned from mlr3superlearner().
newdata
data [data.frame]
A data.frame containing predictors.
...
Unused.
Value
A vector of the predicted values.
See Also
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)
fit <- mlr3superlearner(tmp, "Y", c("glm", "ranger"), "binomial")
predict(fit, tmp)
}