| tune(mlr) | R Documentation |
tune optimizes the hyperparameters of a learner for a classification or regression problem.
Given some ranges for one or more hyperparameters, it estimates the performance
of the learner for each possible combination of the proposed values by
using a resampling method (e.g. cross-validation) and returns the best parameter set and its
performance.
tune(learn.task, resample.instance, ranges, measure)
learn.task |
[learn.task] Specifies the learning task for the problem. |
resample.instance |
[resample.instance] Specifies the training and test indices of the resampled data. |
ranges |
[list] A list of named vectors/lists of possible values for each hyperparameter. You can also pass a list of such ranges by using [ combine.ranges]
in the rare case when it does not make sense to search a complete cross-product of range values. |
measure |
[character/list] Name of performance measure to optimize or a list describing your own performance measure. The default is mean misclassification error. |
A list containing the best parameter set, its aggregated performance over all resampling iterations, a measure of spread and a data frame containing the same values for all evaluated combinations of parameter values.
library(mlr) ct <- make.classif.task(learner="kernlab.svm.classif", data=iris, formula=Species~.) ranges <- list(kernel="rbfdot", C=2^seq(-1,1), sigma=2^seq(-1,1)) ri <- make.cv.instance(size=nrow(iris), iters=3) tune(learn.task=ct, resample.instance=ri, ranges=ranges)