K-fold cross-validation of a ReSurv model

K-fold cross-validation of a ReSurv model

Usage

ReSurvCV(
  IndividualDataPP,
  model,
  hparameters_grid,
  folds,
  random_seed,
  continuous_features_scaling_method = "minmax",
  print_every_n = 1L,
  nrounds = NULL,
  early_stopping_rounds = NULL,
  epochs = 1,
  parallel = FALSE,
  ncores = 1,
  num_workers = 0,
  verbose = FALSE,
  verbose.cv = FALSE,
  cv_data_subsample = 1
)

Arguments

IndividualDataPP

An object of class IndividualDataPP.

model

Character. Either "NN" or "XGB".

hparameters_grid

Named list defining the hyperparameter grid.

folds

Integer. Number of folds.

random_seed

Integer. Random seed.

continuous_features_scaling_method

Character. Scaling method for continuous features.

print_every_n

Integer. Passed to XGBoost.

nrounds

Integer. Number of XGBoost boosting rounds.

early_stopping_rounds

Integer. XGBoost early stopping.

epochs

Integer. Number of NN epochs.

parallel

Logical. Retained for compatibility; execution is sequential and setting this to TRUE produces a warning.

ncores

Integer. Retained for compatibility; currently ignored.

num_workers

Deprecated for the native torch backend. Ignored.

verbose

Logical. Print model fitting output.

verbose.cv

Logical. Print CV progress.

cv_data_subsample

Fraction of training rows used for cross-validation, in (0, 1]. Values greater than 1 and at most 100 are interpreted as percentages. The default 1 uses all rows; use 0.01 for one percent.

Value

An object of class ReSurvCV containing out.cv (all combinations and mean training and validation losses), out.cv.best.oos (the row with smallest validation loss), and hparameters.best, suitable for the hparameters argument of ReSurv(). Cross-validation does not refit the final model.