SuperModel: Super Learner Model

In brian-j-smith/MachineShop: Machine Learning Models and Tools

Super Learner Model

Description

Fit a super learner model to predictions from multiple base learners.

Usage

SuperModel(
  ...,
  model = GBMModel,
  control = MachineShop::settings("control"),
  all_vars = FALSE
)

Arguments

...	model functions, function names, objects; other objects that can be coerced to models; or vector of these to serve as base learners.
model	model function, function name, or object defining the super model; or another object that can be coerced to the model.
control	control function, function name, or object defining the resampling method to be employed for the estimation of base learner weights.
all_vars	logical indicating whether to include the original predictor variables in the super model.

Details

Response types:

factor, numeric, ordered, Surv

Value

SuperModel class object that inherits from MLModel.

References

van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super learner. Statistical Applications in Genetics and Molecular Biology, 6(1).

See Also

fit, resample

Examples

## Requires prior installation of suggested packages gbm and glmnet to run

model <- SuperModel(GBMModel, SVMRadialModel, GLMNetModel(lambda = 0.01))
model_fit <- fit(sale_amount ~ ., data = ICHomes, model = model)
predict(model_fit, newdata = ICHomes)

brian-j-smith/MachineShop documentation built on Sept. 22, 2023, 10:01 p.m.