ensemble.deepregression: Ensemblind deepregression models

In deepregression: Fitting Deep Distributional Regression

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)

deepregression documentation built on Jan. 18, 2023, 1:11 a.m.