SuperModel {MachineShop} | R Documentation |
Fit a super learner model to predictions from multiple base learners.
SuperModel( ..., model = GBMModel, control = MachineShop::settings("control"), all_vars = FALSE )
... |
model functions, function names, calls, or vector of these to serve as base learners. |
model |
model function, function name, or call defining the super model. |
control |
control function, function name, or call 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. |
factor
, numeric
, ordered
,
Surv
SuperModel
class object that inherits from MLModel
.
van der Lann, M.J., Hubbard A.E. (2007) Super Learner. Statistical Applications in Genetics and Molecular Biology, 6(1).
## 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)