hyperopt: Tune Hyperparameters for a machine learning task

In jakob-r/mlrHyperopt: Easy Hyperparameteroptimization with mlr and mlrMBO

Description

Tunes the Hyperparameters for a given task and learner. Tries to find the best parameter set to tune for the given learner.

Usage

hyperopt(
  task,
  learner = NULL,
  par.config = NULL,
  hyper.control = NULL,
  show.info = getMlrOptions()$show.info
)

Arguments

task	[Task] The Task
learner	[Learner] The learner that is subject to the Hyperparameter Tuning. If no learner is given the learner referenced in the par.config will be used, if available.
par.config	[ParConfig] The Parameter Configuration
hyper.control	[HyperControl] The Hyperparameter Control Object
show.info	[logical(1)] Print verbose output on console? Default is set via configureMlr.

Value

[TuneResult]

Examples

# the shortest way of hyperparameter optimization
hyperopt(iris.task, "classif.svm")

# manually defining the paramer space configuration
par.config = makeParConfig(
  par.set = makeParamSet(
    makeIntegerParam("mtry", lower = 1, upper = 4),
    makeDiscreteParam("ntree", values = c(10, 25, 50))
  ),
  par.vals = list(replace = FALSE),
  learner.name = "randomForest"
)
hyperopt(bh.task, par.config = par.config)

jakob-r/mlrHyperopt documentation built on Jan. 10, 2022, 4:32 p.m.