ensemble.deepregression: Ensembling deepregression models

Description

Ensembling deepregression models

Usage

# S3 method for 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),
  ...
)

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)

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