Parent class for PipeOps that transform Prediction objects to different types.
Input and Output Channels
PipeOpPredTransformer has one input and output channel named "input" and "output".
In training and testing these expect and produce mlr3::Prediction objects with the type
depending on the transformers.
State
The $state is a named list with the $state elements
inpredtypes: Predict types in the input prediction object during training.outpredtypes: Predict types in the input prediction object during prediction, checked againstinpredtypes.
Internals
Classes inheriting from PipeOpPredTransformer transform Prediction
objects from one class (e.g. regr, classif) to another.
See also
Other PipeOps:
PipeOpTaskTransformer,
PipeOpTransformer,
mlr_pipeops_survavg,
mlr_pipeops_trafopred_classifsurv_IPCW,
mlr_pipeops_trafopred_classifsurv_disctime,
mlr_pipeops_trafopred_regrsurv,
mlr_pipeops_trafopred_survregr,
mlr_pipeops_trafotask_regrsurv,
mlr_pipeops_trafotask_survclassif_IPCW,
mlr_pipeops_trafotask_survclassif_disctime,
mlr_pipeops_trafotask_survregr
Other Transformers:
PipeOpTaskTransformer,
PipeOpTransformer
Super classes
mlr3pipelines::PipeOp -> mlr3proba::PipeOpTransformer -> PipeOpPredTransformer
Methods
Method new()
Creates a new instance of this R6 class.
Usage
PipeOpPredTransformer$new(
id,
param_set = ps(),
param_vals = list(),
packages = character(0),
input = data.table(),
output = data.table()
)Arguments
id(
character(1))
Identifier of the resulting object.param_set(paradox::ParamSet)
Set of hyperparameters.param_vals(
list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.packages(
character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand viarequireNamespace().inputdata.table::data.table
data.tablewith columnsname(character),train(character),predict(character). Sets the$inputslot, see PipeOp.outputdata.table::data.table
data.tablewith columnsname(character),train(character),predict(character). Sets the$outputslot, see PipeOp.