This is a CPOConstructor
to be used to create a
CPO
. It is called like any R function and returns
the created CPO
.
Apply a given function to the target column of a regression Task
.
cpoApplyFunRegrTarget( trafo, invert.response = NULL, invert.se = NULL, param = NULL, vectorize = TRUE, gauss.points = 23, id, export = "export.default", affect.type = NULL, affect.index = integer(0), affect.names = character(0), affect.pattern = NULL, affect.invert = FALSE, affect.pattern.ignore.case = FALSE, affect.pattern.perl = FALSE, affect.pattern.fixed = FALSE )
trafo | [ The function must take one or two arguments. If it takes two arguments, the second argument
will be |
---|---|
invert.response | [ Similarly to This can also be Default is |
invert.se | [
Default is |
param | [any] |
vectorize | [ |
gauss.points | [ |
id | [ |
export | [ |
affect.type | [ |
affect.index | [ |
affect.names | [ |
affect.pattern | [ |
affect.invert | [ |
affect.pattern.ignore.case | [ |
affect.pattern.perl | [ |
affect.pattern.fixed | [ |
[CPO
].
When both mean
and se
prediction is available, it may be possible to
make more accurate mean inversion than for the response
predict.type
,
using integrals or approximations like the delta method. In such cases it may be
advisable to prepend this CPO
with the cpoResponseFromSE
CPO
.
Note when trafo
or invert.response
take more than one argument, the
second argument will be set to the value of param
. This may lead to unexpected
results when using functions with rarely used parameters, e.g. log
.
In these cases, it may be necessary to wrap the function:
trafo = function(x) log(x)
.
This function creates a CPO object, which can be applied to
Task
s, data.frame
s, link{Learner}
s
and other CPO objects using the %>>%
operator.
The parameters of this object can be changed after creation
using the function setHyperPars
. The other
hyper-parameter manipulating functins, getHyperPars
and getParamSet
similarly work as one expects.
If the “id” parameter is given, the hyperparameters will have this id as aprefix; this will, however, not change the parameters of the creator function.
CPOConstructor
CPO constructor functions are called with optional values of parameters, and additional “special” optional values.
The special optional values are the id
parameter, and the affect.*
parameters. The affect.*
parameters
enable the user to control which subset of a given dataset is affected. If no affect.*
parameters are given, all
data features are affected by default.
Other CPOs:
cpoApplyFun()
,
cpoAsNumeric()
,
cpoCache()
,
cpoCbind()
,
cpoCollapseFact()
,
cpoDropConstants()
,
cpoDummyEncode()
,
cpoFilterAnova()
,
cpoFilterCarscore()
,
cpoFilterChiSquared()
,
cpoFilterFeatures()
,
cpoFilterGainRatio()
,
cpoFilterInformationGain()
,
cpoFilterKruskal()
,
cpoFilterLinearCorrelation()
,
cpoFilterMrmr()
,
cpoFilterOneR()
,
cpoFilterPermutationImportance()
,
cpoFilterRankCorrelation()
,
cpoFilterRelief()
,
cpoFilterRfCImportance()
,
cpoFilterRfImportance()
,
cpoFilterRfSRCImportance()
,
cpoFilterRfSRCMinDepth()
,
cpoFilterSymmetricalUncertainty()
,
cpoFilterUnivariate()
,
cpoFilterVariance()
,
cpoFixFactors()
,
cpoIca()
,
cpoImpactEncodeClassif()
,
cpoImpactEncodeRegr()
,
cpoImputeConstant()
,
cpoImputeHist()
,
cpoImputeLearner()
,
cpoImputeMax()
,
cpoImputeMean()
,
cpoImputeMedian()
,
cpoImputeMin()
,
cpoImputeMode()
,
cpoImputeNormal()
,
cpoImputeUniform()
,
cpoImpute()
,
cpoLogTrafoRegr()
,
cpoMakeCols()
,
cpoMissingIndicators()
,
cpoModelMatrix()
,
cpoOversample()
,
cpoPca()
,
cpoProbEncode()
,
cpoQuantileBinNumerics()
,
cpoRegrResiduals()
,
cpoResponseFromSE()
,
cpoSample()
,
cpoScaleMaxAbs()
,
cpoScaleRange()
,
cpoScale()
,
cpoSelect()
,
cpoSmote()
,
cpoSpatialSign()
,
cpoTransformParams()
,
cpoWrap()
,
makeCPOCase()
,
makeCPOMultiplex()