mbo_tramnet: Model based optimization for regularized transformation...
In tramnet: Penalized Transformation Models

mbo_tramnet

R Documentation

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, ...)

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