mbo_tramnet: Model based optimization for regularized transformation...

In tramnet: Penalized Transformation Models

Model based optimization for regularized transformation models

Description

Uses model based optimization to find the optimal tuning parameter(s) in a regularized transformation model based on cross-validated log-likelihoods. Here the 'tramnet' package makes use of the 'mlr3mbo' interface for Bayesian Optimization in machine learning problems to maximize the cv-logLik as a black-box function of the tuning parameters alpha and lambda.

Usage

mbo_tramnet(
  object,
  fold = 2,
  n_iter = 5,
  minlambda = 0,
  maxlambda = 16,
  minalpha = 0,
  maxalpha = 1,
  folds = NULL,
  noisy = FALSE,
  obj_type = c("lasso", "ridge", "elnet"),
  verbose = TRUE,
  ...
)

Arguments

object	Object of class "tramnet".
fold	Number of cross validation folds.
n_iter	Maximum number of iterations in model-based optimization routine.
minlambda	Minimum value for lambda (default minlambda = 0).
maxlambda	Maximum value for lambda (default maxlambda = 16).
minalpha	Minimum value for alpha (default minalpha = 0).
maxalpha	Maximum value for alpha (default maxalpha = 1).
folds	Self specified folds for cross validation (mainly for reproducibility and comparability purposes).
noisy	indicates whether folds for k-fold cross-validation should be random for each iteration, leading to a noisy objective function (default noisy = FALSE).
obj_type	Objective type, one of "lasso", "ridge" or "elnet".
verbose	Toggle for a verbose output (default verbose = TRUE)
...	Currently ignored.

Value

See Optimizer's optimize function which returns a data.table::data.table.

tramnet documentation built on Nov. 4, 2023, 3 p.m.