This is a CPOConstructor
to be used to create a
CPO
. It is called like any R function and returns
the created CPO
.
Oversamples the minor or undersamples the major class in
a binary classification task to alleviate class imbalance.
Uses mlr::oversample
and
mlr::undersample
, see documentation
there.
cpoOversample(rate = NULL, cl = NULL, id, export = "export.default")cpoUndersample(rate = NULL, cl = NULL, id, export = "export.default")
[numeric(1)
| NULL
]
Factor to up- or downsample a class. Must be between 0
and 1 for undersampling and greater or equal 1 for oversampling.
If this is NULL
, this is the ratio of major to minor
class prevalence (for oversampling, or the inverse for undersampling).
Must not be NULL
if cl
is not NULL
and not the
minor class for oversampling / the major class for undersampling.
Default is NULL
.
[character(1)
| NULL
]
Class to over- or undersample. For NULL
, the minor class
for oversampling or the major class for undersampling is chosen
automatically.
[character(1)
]
id to use as prefix for the CPO's hyperparameters. this
must be used to avoid name clashes when composing two
CPOs of the same type, or with learners or other CPOS
with hyperparameters with clashing names.
[character
]
Either a character vector indicating the parameters to
export as hyperparameters, or one of the special values
“export.all” (export all parameters),
“export.default” (export all parameters that are exported by default),
“export.set” (export all parameters that were set during construction),
“export.default.set” (export the intersection of the “default” and “set” parameters),
“export.unset” (export all parameters that were not set during construction) or
“export.default.unset” (export the intersection of the “default” and “unset” parameters).
Default is “export.default”.
[CPO
].
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.
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: cpoApplyFunRegrTarget
,
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
, cpoPca
,
cpoProbEncode
,
cpoQuantileBinNumerics
,
cpoRegrResiduals
,
cpoResponseFromSE
, cpoSample
,
cpoScaleMaxAbs
,
cpoScaleRange
, cpoScale
,
cpoSelect
, cpoSmote
,
cpoSpatialSign
,
cpoTransformParams
, cpoWrap
,
makeCPOCase
, makeCPOMultiplex