resolution,
or discretize them yourself by always using makeDiscreteParam
in the par.set passed to tuneParams.nsga2.
Can handle numeric(vector) and integer(vector) hyperparameters, but no dependencies.
For integers the internally proposed numeric values are automatically rounded.makeTuneMultiCritControlGrid(same.resampling.instance = TRUE,
resolution = 10L, log.fun = NULL, final.dw.perc = NULL, budget = NULL)makeTuneMultiCritControlNSGA2(same.resampling.instance = TRUE,
impute.val = NULL, log.fun = NULL, final.dw.perc = NULL,
budget = NULL, ...)
makeTuneMultiCritControlRandom(same.resampling.instance = TRUE,
maxit = 100L, log.fun = NULL, final.dw.perc = NULL, budget = NULL)
logical(1)]
Should the same resampling instance be used for all evaluations to reduce variance?
Default is TRUE.integer]
Resolution of the grid for each numeric/integer parameter in par.set.
For vector parameters, it is the resolution per dimension.
Either pass one resolution for all parameters, or a named vector.
See generateGridDesign.
Default is 10.function | NULL]
Function used for logging. If set to NULL, the internal default will be used.
Otherwise a function with arguments learner, resampling, measures,
par.set, control, opt.path, dob, x, y, remove.nas,
and 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.boolean]
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.
Default is NULL which will not change anything.integer(1)]
Maximum budget for tuning. This value restricts the number of function
evaluations. In case of makeTuneMultiCritControlGrid this number
must be identical to the size of the grid. For
makeTuneMultiCritControlRandom the budget equals the number
of iterations (maxit) performed by the random search algorithm.
And in case of makeTuneMultiCritControlNSGA2 the budget
corresponds to the product of the maximum number of generations
(max(generations)) + 1 (for the initial population) and the size of
the population (popsize).numeric]
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.
It is not stored in the optimization path, an NA and a corresponding error message are
logged instead.
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 Inf instead.
For multi-criteria optimization pass a vector of imputation values, one for each of your measures,
in the same order as your measures.integer(1)]
Number of iterations for random search.
Default is 100.TuneMultiCritControl]. The specific subclass is one of
TuneMultiCritControlGrid, TuneMultiCritControlRandom,
TuneMultiCritControlNSGA2.plotTuneMultiCritResultGGVIS,
plotTuneMultiCritResult,
tuneParamsMultiCrit