ensemble.deepregression {deepregression}

Ensemblind deepregression models

Description

Ensemblind deepregression models

Usage

## S3 method for class 'deepregression'
ensemble(
  x,
  n_ensemble = 5,
  reinitialize = TRUE,
  mylapply = lapply,
  verbose = FALSE,
  patience = 20,
  plot = TRUE,
  print_members = TRUE,
  stop_if_nan = TRUE,
  save_weights = TRUE,
  callbacks = list(),
  save_fun = NULL,
  seed = seq_len(n_ensemble),
  ...
)

Arguments

x

object of class "deepregression" to ensemble

n_ensemble

numeric; number of ensemble members to fit

reinitialize

logical; if TRUE (default), model weights are initialized randomly prior to fitting each member. Fixed weights are not affected

mylapply

lapply function to be used; defaults to lapply

verbose

whether to print training in each fold

patience

number of patience for early stopping

plot

whether to plot the resulting losses in each fold

print_members

logical; print results for each member

stop_if_nan

logical; whether to stop CV if NaN values occur

save_weights

whether to save final weights of each ensemble member; defaults to TRUE

callbacks

a list of callbacks used for fitting

save_fun

function applied to the model in each fold to be stored in the final result

seed

seed for reproducibility

...

further arguments passed to object$fit_fun

Value

object of class "drEnsemble", containing the original "deepregression" model together with a list of ensembling results (training history and, if save_weights is TRUE, the trained weights of each ensemble member)