Extracts principle components from data. Only affects numerical features.
See `stats::prcomp()`

for details.

`R6Class`

object inheriting from `PipeOpTaskPreproc`

/`PipeOp`

.

PipeOpPCA$new(id = "pca", param_vals = list())

`id`

::`character(1)`

Identifier of resulting object, default`"pca"`

.`param_vals`

:: named`list`

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

Input and output channels are inherited from `PipeOpTaskPreproc`

.

The output is the input `Task`

with all affected numeric features replaced by their principal components.

The `$state`

is a named `list`

with the `$state`

elements inherited from `PipeOpTaskPreproc`

, as well as the elements of the class stats::prcomp,
with the exception of the `$x`

slot. These are in particular:

`sdev`

::`numeric`

The standard deviations of the principal components.`rotation`

::`matrix`

The matrix of variable loadings.`center`

::`numeric`

|`logical(1)`

The centering used, or`FALSE`

.`scale`

::`numeric`

|`logical(1)`

The scaling used, or`FALSE`

.

The parameters are the parameters inherited from `PipeOpTaskPreproc`

, as well as:

`center`

::`logical(1)`

Indicating whether the features should be centered. Default is`FALSE`

. See`prcomp()`

.`scale.`

::`logical(1)`

Whether to scale features to unit variance before analysis. Default is`FALSE`

, but scaling is advisable. See`prcomp()`

.`rank.`

::`integer(1)`

Maximal number of principal components to be used. Default is`NULL`

: use all components. See`prcomp()`

.

Uses the `prcomp()`

function.

Only methods inherited from `PipeOpTaskPreproc`

/`PipeOp`

.

https://mlr3book.mlr-org.com/list-pipeops.html

Other PipeOps:
`PipeOpEnsemble`

,
`PipeOpImpute`

,
`PipeOpTargetTrafo`

,
`PipeOpTaskPreprocSimple`

,
`PipeOpTaskPreproc`

,
`PipeOp`

,
`mlr_pipeops_boxcox`

,
`mlr_pipeops_branch`

,
`mlr_pipeops_chunk`

,
`mlr_pipeops_classbalancing`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_classweights`

,
`mlr_pipeops_colapply`

,
`mlr_pipeops_collapsefactors`

,
`mlr_pipeops_colroles`

,
`mlr_pipeops_copy`

,
`mlr_pipeops_datefeatures`

,
`mlr_pipeops_encodeimpact`

,
`mlr_pipeops_encodelmer`

,
`mlr_pipeops_encode`

,
`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_proxy`

,
`mlr_pipeops_quantilebin`

,
`mlr_pipeops_randomprojection`

,
`mlr_pipeops_randomresponse`

,
`mlr_pipeops_regravg`

,
`mlr_pipeops_removeconstants`

,
`mlr_pipeops_renamecolumns`

,
`mlr_pipeops_replicate`

,
`mlr_pipeops_scalemaxabs`

,
`mlr_pipeops_scalerange`

,
`mlr_pipeops_scale`

,
`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`

,
`mlr_pipeops`

library("mlr3") task = tsk("iris") pop = po("pca") task$data() #> Species Petal.Length Petal.Width Sepal.Length Sepal.Width #> 1: setosa 1.4 0.2 5.1 3.5 #> 2: setosa 1.4 0.2 4.9 3.0 #> 3: setosa 1.3 0.2 4.7 3.2 #> 4: setosa 1.5 0.2 4.6 3.1 #> 5: setosa 1.4 0.2 5.0 3.6 #> --- #> 146: virginica 5.2 2.3 6.7 3.0 #> 147: virginica 5.0 1.9 6.3 2.5 #> 148: virginica 5.2 2.0 6.5 3.0 #> 149: virginica 5.4 2.3 6.2 3.4 #> 150: virginica 5.1 1.8 5.9 3.0 pop$train(list(task))[[1]]$data() #> Species PC1 PC2 PC3 PC4 #> 1: setosa -2.684126 0.31939725 -0.02791483 -0.002262437 #> 2: setosa -2.714142 -0.17700123 -0.21046427 -0.099026550 #> 3: setosa -2.888991 -0.14494943 0.01790026 -0.019968390 #> 4: setosa -2.745343 -0.31829898 0.03155937 0.075575817 #> 5: setosa -2.728717 0.32675451 0.09007924 0.061258593 #> --- #> 146: virginica 1.944110 0.18753230 0.17782509 -0.426195940 #> 147: virginica 1.527167 -0.37531698 -0.12189817 -0.254367442 #> 148: virginica 1.764346 0.07885885 0.13048163 -0.137001274 #> 149: virginica 1.900942 0.11662796 0.72325156 -0.044595305 #> 150: virginica 1.390189 -0.28266094 0.36290965 0.155038628 pop$state #> Standard deviations (1, .., p=4): #> [1] 2.0562689 0.4926162 0.2796596 0.1543862 #> #> Rotation (n x k) = (4 x 4): #> PC1 PC2 PC3 PC4 #> Petal.Length 0.85667061 -0.17337266 0.07623608 0.4798390 #> Petal.Width 0.35828920 -0.07548102 0.54583143 -0.7536574 #> Sepal.Length 0.36138659 0.65658877 -0.58202985 -0.3154872 #> Sepal.Width -0.08452251 0.73016143 0.59791083 0.3197231