A basic grid search can handle all kinds of parameter types.
You can either use their correct param type and
or discretize them yourself by always using ParamHelpers::makeDiscreteParam
par.set passed to tuneParams.
same.resampling.instance = TRUE,
impute.val = NULL,
resolution = 10L,
tune.threshold = FALSE,
tune.threshold.args = list(),
log.fun = "default",
final.dw.perc = NULL,
budget = NULL
Should the same resampling instance be used for all evaluations to reduce variance?
If something goes wrong during optimization (e.g. the learner crashes),
this value is fed back to the tuner, so the tuning algorithm does not abort.
Imputation is only active if
on.learner.error is configured not to stop in configureMlr.
It is not stored in the optimization path, an NA and a corresponding error message are
Note that this value is later multiplied by -1 for maximization measures internally, so you
need to enter a larger positive value for maximization here as well.
Default is the worst obtainable value of the performance measure you optimize for when
you aggregate by mean value, or
For multi-criteria optimization pass a vector of imputation values, one for each of your measures,
in the same order as your measures.
Resolution of the grid for each numeric/integer parameter in
For vector parameters, it is the resolution per dimension.
Either pass one resolution for all parameters, or a named vector.
Default is 10.
Should the threshold be tuned for the measure at hand, after each hyperparameter evaluation,
Only works for classification if the predict type is “prob”.
Further arguments for threshold tuning that are passed down to tuneThreshold.
Default is none.
Function used for logging. If set to “default” (the default), the evaluated design points, the resulting
performances, and the runtime will be reported.
If set to “memory” the memory usage for each evaluation will also be displayed, with
character(1) small increase
in run time.
character(1) function with arguments
prev.stage is expected.
The default displays the performance measures, the time needed for evaluating,
the currently used memory and the max memory ever used before
(the latter two both taken from gc).
See the implementation for details.
If a Learner wrapped by a makeDownsampleWrapper is used, you can define the value of
dw.perc which is used to train the Learner with the final parameter setting found by the tuning.
NULL which will not change anything.
Maximum budget for tuning. This value restricts the number of function
evaluations. If set, must equal the size of the grid.