mlr (version 2.7)

getCaretParamSet: Get tuning parameters from a learner of the caret R-package.

Description

Constructs a grid of tuning parameters from a learner of the caret R-package. These values are then converted into a list of non-tunable parameters (par.vals) and a tunable ParamSet (par.set), which can be used by tuneParams for tuning the learner. Numerical parameters will either be specified by their lower and upper bounds or they will be discretized into specific values.

Usage

getCaretParamSet(learner, length = 3L, task, discretize = TRUE)

Value

[list(2)]. A list of parameters:

• par.vals

contains a list of all constant tuning parameterspar.setis a ParamSet, containing all the configurable tuning parameters

Examples

library(caret)
classifTask = makeClassifTask(data = iris, target = "Species")

# (1) classification (random forest) with discretized parameters
getCaretParamSet("rf", length = 9L, task = classifTask, discretize = TRUE)

# (2) regression (gradient boosting machine) without discretized parameters
library(mlbench)
data(BostonHousing)
regrTask = makeRegrTask(data = BostonHousing, target = "medv")
getCaretParamSet("gbm", length = 9L, task = regrTask, discretize = FALSE)