mbo_tramnet: 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 mlrMBO 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_design = 5, n_iter = 5,
  minlambda = 0, maxlambda = 16, minalpha = 0, maxalpha = 1,
  folds = NULL, learner = "regr.km", pred.type = "se",
  opt_crit = makeMBOInfillCritEI(), noisy = FALSE,
  obj_type = c("lasso", "ridge", "elnet"), verbose = TRUE, ...)

Value

returns an object of class "MBOSingleObjResult" which is documented in mbo

Arguments

object: object of class tramnet
fold: fold for cross validation
n_design: results in n_design times the number of tuning parameters rows for the initial design matrix based on a random latin hypercube design
n_iter: number of iterations in the model based optimization procedure
minlambda: minimum value for lambda (default: 0)
maxlambda: maximum value for lambda (default: 16)
minalpha: minimum value for alpha (default: 0)
maxalpha: maximum value for alpha (default: 1)
folds: self specified folds for cross validation (mainly for reproducibility and comparability purposes)
learner: type of leaner used for the optimization (default: "regr.km")
pred.type: prediction type of the learner (default: "se")
opt_crit: optimization criterion, default: expected improvement
noisy: indicates whether folds for k-fold cross-validation should be random for each iteration, leading to a noisy objective function (default: FALSE)
obj_type: objective type, one of "lasso", "ridge" or "elnet"
verbose: toggle for a verbose output (default: TRUE)
...: additional arguments are ignored