| mlr_pipeops_classweights {mlr3pipelines} | R Documentation |
Class Weights for Sample Weighting
Description
Adds a class weight column to the Task that different Learners may be
able to use for sample weighting. Sample weights are added to each sample according to the target class.
Only binary classification tasks are supported.
Caution: when constructed naively without parameter, the weights are all set to 1. The minor_weight parameter
must be adjusted for this PipeOp to be useful.
Format
R6Class object inheriting from PipeOpTaskPreproc/PipeOp.
Construction
PipeOpClassWeights$new(id = "classweights", param_vals = list())
-
id::character(1)Identifier of the resulting object, default"classweights" -
param_vals:: namedlist
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist().
Input and Output Channels
Input and output channels are inherited from PipeOpTaskPreproc. Instead of a Task, a
TaskClassif is used as input and output during training and prediction.
The output during training is the input Task with added weights column according to target class.
The output during prediction is the unchanged input.
State
The $state is a named list with the $state elements inherited from PipeOpTaskPreproc.
Parameters
The parameters are the parameters inherited from PipeOpTaskPreproc; however, the affect_columns parameter is not present. Further parameters are:
-
minor_weight::numeric(1)
Weight given to samples of the minor class. Major class samples have weight 1. Initialized to 1.
Internals
Introduces, or overwrites, the "weights" column in the Task. However, the Learner method needs to
respect weights for this to have an effect.
The newly introduced column is named .WEIGHTS; there will be a naming conflict if this column already exists and is not a
weight column itself.
Fields
Only fields inherited from PipeOpTaskPreproc/PipeOp.
Methods
Only methods inherited from PipeOpTaskPreproc/PipeOp.
See Also
https://mlr-org.com/pipeops.html
Other PipeOps:
PipeOp,
PipeOpEnsemble,
PipeOpImpute,
PipeOpTargetTrafo,
PipeOpTaskPreproc,
PipeOpTaskPreprocSimple,
mlr_pipeops,
mlr_pipeops_boxcox,
mlr_pipeops_branch,
mlr_pipeops_chunk,
mlr_pipeops_classbalancing,
mlr_pipeops_classifavg,
mlr_pipeops_colapply,
mlr_pipeops_collapsefactors,
mlr_pipeops_colroles,
mlr_pipeops_copy,
mlr_pipeops_datefeatures,
mlr_pipeops_encode,
mlr_pipeops_encodeimpact,
mlr_pipeops_encodelmer,
mlr_pipeops_featureunion,
mlr_pipeops_filter,
mlr_pipeops_fixfactors,
mlr_pipeops_histbin,
mlr_pipeops_ica,
mlr_pipeops_imputeconstant,
mlr_pipeops_imputehist,
mlr_pipeops_imputelearner,
mlr_pipeops_imputemean,
mlr_pipeops_imputemedian,
mlr_pipeops_imputemode,
mlr_pipeops_imputeoor,
mlr_pipeops_imputesample,
mlr_pipeops_kernelpca,
mlr_pipeops_learner,
mlr_pipeops_missind,
mlr_pipeops_modelmatrix,
mlr_pipeops_multiplicityexply,
mlr_pipeops_multiplicityimply,
mlr_pipeops_mutate,
mlr_pipeops_nmf,
mlr_pipeops_nop,
mlr_pipeops_ovrsplit,
mlr_pipeops_ovrunite,
mlr_pipeops_pca,
mlr_pipeops_proxy,
mlr_pipeops_quantilebin,
mlr_pipeops_randomprojection,
mlr_pipeops_randomresponse,
mlr_pipeops_regravg,
mlr_pipeops_removeconstants,
mlr_pipeops_renamecolumns,
mlr_pipeops_replicate,
mlr_pipeops_scale,
mlr_pipeops_scalemaxabs,
mlr_pipeops_scalerange,
mlr_pipeops_select,
mlr_pipeops_smote,
mlr_pipeops_spatialsign,
mlr_pipeops_subsample,
mlr_pipeops_targetinvert,
mlr_pipeops_targetmutate,
mlr_pipeops_targettrafoscalerange,
mlr_pipeops_textvectorizer,
mlr_pipeops_threshold,
mlr_pipeops_tunethreshold,
mlr_pipeops_unbranch,
mlr_pipeops_updatetarget,
mlr_pipeops_vtreat,
mlr_pipeops_yeojohnson
Examples
library("mlr3")
task = tsk("spam")
opb = po("classweights")
# task weights
task$weights
# double the instances in the minority class (spam)
opb$param_set$values$minor_weight = 2
result = opb$train(list(task))[[1L]]
result$weights