# mlr3pipelines

Package website: [release](https://mlr3pipelines.mlr-org.com/) \|
[dev](https://mlr3pipelines.mlr-org.com/dev/)

Dataflow Programming for Machine Learning in R.

## What is `mlr3pipelines`?

Watch our “WhyR 2020” Webinar Presentation on Youtube for an
introduction! Find the slides
[here](https://raw.githubusercontent.com/mlr-org/mlr-outreach/main/2020_whyr/slides.pdf).

[![WhyR 2020
mlr3pipelines](https://img.youtube.com/vi/4r8K3GO5wk4/0.jpg)](https://www.youtube.com/watch?v=4r8K3GO5wk4)

**`mlr3pipelines`** is a [dataflow
programming](https://en.wikipedia.org/wiki/Dataflow_programming) toolkit
for machine learning in R utilising the
**[mlr3](https://github.com/mlr-org/mlr3)** package. Machine learning
workflows can be written as directed “Graphs” that represent data flows
between preprocessing, model fitting, and ensemble learning units in an
expressive and intuitive language. Using methods from the
**[mlr3tuning](https://github.com/mlr-org/mlr3tuning)** package, it is
even possible to simultaneously optimize parameters of multiple
processing units.

In principle, *mlr3pipelines* is about defining singular data and model
manipulation steps as “PipeOps”:

``` r
pca        = po("pca")
filter     = po("filter", filter = mlr3filters::flt("variance"), filter.frac = 0.5)
learner_po = po("learner", learner = lrn("classif.rpart"))
```

These pipeops can then be combined together to define machine learning
pipelines. These can be wrapped in a `GraphLearner` that behave like any
other `Learner` in `mlr3`.

``` r
graph = pca %>>% filter %>>% learner_po
glrn = GraphLearner$new(graph)
```

This learner can be used for resampling, benchmarking, and even tuning.

``` r
resample(tsk("iris"), glrn, rsmp("cv"))
#> <ResampleResult> with 10 resampling iterations
#>  task_id                 learner_id resampling_id iteration warnings errors
#>     iris pca.variance.classif.rpart            cv         1        0      0
#>     iris pca.variance.classif.rpart            cv         2        0      0
#>     iris pca.variance.classif.rpart            cv         3        0      0
#>     iris pca.variance.classif.rpart            cv         4        0      0
#>     iris pca.variance.classif.rpart            cv         5        0      0
#>     iris pca.variance.classif.rpart            cv         6        0      0
#>     iris pca.variance.classif.rpart            cv         7        0      0
#>     iris pca.variance.classif.rpart            cv         8        0      0
#>     iris pca.variance.classif.rpart            cv         9        0      0
#>     iris pca.variance.classif.rpart            cv        10        0      0
```

## Feature Overview

Single computational steps can be represented as so-called **PipeOps**,
which can then be connected with directed edges in a **Graph**. The
scope of *mlr3pipelines* is still growing; currently supported features
are:

- Simple data manipulation and preprocessing operations, e.g. PCA,
  feature filtering
- Task subsampling for speed and outcome class imbalance handling
- *mlr3* *Learner* operations for prediction and stacking
- Simultaneous path branching (data going both ways)
- Alternative path branching (data going one specific way, controlled by
  hyperparameters)
- Ensemble methods and aggregation of predictions

## Documentation

A good way to get into `mlr3pipelines` are the following two vignettes:

- [Sequential
  Pipelines](https://mlr3book.mlr-org.com/chapters/chapter7/sequential_pipelines.html)
- [Non-Sequential Pipelines and
  Tuning](https://mlr3book.mlr-org.com/chapters/chapter8/non-sequential_pipelines_and_tuning.html)

## Bugs, Questions, Feedback

*mlr3pipelines* is a free and open source software project that
encourages participation and feedback. If you have any issues,
questions, suggestions or feedback, please do not hesitate to open an
“issue” about it on the GitHub page!

In case of problems / bugs, it is often helpful if you provide a
“minimum working example” that showcases the behaviour (but don’t worry
about this if the bug is obvious).

Please understand that the resources of the project are limited:
response may sometimes be delayed by a few days, and some feature
suggestions may be rejected if they are deemed too tangential to the
vision behind the project.

## Citing mlr3pipelines

If you use mlr3pipelines, please cite our [JMLR
article](https://jmlr.org/papers/v22/21-0281.html):

``` R
@Article{mlr3pipelines,
  title = {{mlr3pipelines} - Flexible Machine Learning Pipelines in R},
  author = {Martin Binder and Florian Pfisterer and Michel Lang and Lennart Schneider and Lars Kotthoff and Bernd Bischl},
  journal = {Journal of Machine Learning Research},
  year = {2021},
  volume = {22},
  number = {184},
  pages = {1-7},
  url = {https://jmlr.org/papers/v22/21-0281.html},
}
```

## Similar Projects

A predecessor to this package is the
[*mlrCPO*-package](https://github.com/mlr-org/mlrCPO), which works with
*mlr* 2.x. Other packages that provide, to varying degree, some
preprocessing functionality or machine learning domain specific
language, are the *[caret](https://github.com/topepo/caret)* package and
the related *[recipes](https://recipes.tidymodels.org/)* project, and
the *[dplyr](https://github.com/tidyverse/dplyr)* package.

# Package index

## Package

- [`mlr3pipelines`](https://mlr3pipelines.mlr-org.com/reference/mlr3pipelines-package.md)
  [`mlr3pipelines-package`](https://mlr3pipelines.mlr-org.com/reference/mlr3pipelines-package.md)
  : mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

## Building Blocks

- [`PipeOp`](https://mlr3pipelines.mlr-org.com/reference/PipeOp.md) :
  PipeOp Base Class
- [`Graph`](https://mlr3pipelines.mlr-org.com/reference/Graph.md) :
  Graph Base Class
- [`PipeOpTaskPreproc`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTaskPreproc.md)
  : Task Preprocessing Base Class
- [`PipeOpTaskPreprocSimple`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTaskPreprocSimple.md)
  : Simple Task Preprocessing Base Class
- [`Multiplicity()`](https://mlr3pipelines.mlr-org.com/reference/Multiplicity.md)
  : Multiplicity

## Graph Tools

- [`` `%>>%` ``](https://mlr3pipelines.mlr-org.com/reference/grapes-greater-than-greater-than-grapes.md)
  [`concat_graphs()`](https://mlr3pipelines.mlr-org.com/reference/grapes-greater-than-greater-than-grapes.md)
  [`` `%>>!%` ``](https://mlr3pipelines.mlr-org.com/reference/grapes-greater-than-greater-than-grapes.md)
  : PipeOp Composition Operator
- [`gunion()`](https://mlr3pipelines.mlr-org.com/reference/gunion.md) :
  Disjoint Union of Graphs
- [`greplicate()`](https://mlr3pipelines.mlr-org.com/reference/greplicate.md)
  : Create Disjoint Graph Union of Copies of a Graph
- [`chain_graphs()`](https://mlr3pipelines.mlr-org.com/reference/chain_graphs.md)
  : Chain a Series of Graphs

## PipeOps

- [`mlr_pipeops`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops.md)
  : Dictionary of PipeOps
- [`po()`](https://mlr3pipelines.mlr-org.com/reference/po.md)
  [`pos()`](https://mlr3pipelines.mlr-org.com/reference/po.md) :
  Shorthand PipeOp Constructor
- [`mlr_pipeops_adas`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_adas.md)
  [`PipeOpADAS`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_adas.md)
  : ADAS Balancing
- [`mlr_pipeops_blsmote`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_blsmote.md)
  [`PipeOpBLSmote`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_blsmote.md)
  : BLSMOTE Balancing
- [`mlr_pipeops_boxcox`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_boxcox.md)
  [`PipeOpBoxCox`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_boxcox.md)
  : Box-Cox Transformation of Numeric Features
- [`mlr_pipeops_branch`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_branch.md)
  [`PipeOpBranch`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_branch.md)
  : Path Branching
- [`mlr_pipeops_chunk`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_chunk.md)
  [`PipeOpChunk`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_chunk.md)
  : Chunk Input into Multiple Outputs
- [`mlr_pipeops_classbalancing`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classbalancing.md)
  [`PipeOpClassBalancing`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classbalancing.md)
  : Class Balancing
- [`mlr_pipeops_classifavg`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classifavg.md)
  [`PipeOpClassifAvg`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classifavg.md)
  : Majority Vote Prediction
- [`mlr_pipeops_classweights`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classweights.md)
  [`PipeOpClassWeights`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classweights.md)
  : Class Weights for Sample Weighting
- [`mlr_pipeops_classweightsex`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classweightsex.md)
  [`PipeOpClassWeightsEx`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_classweightsex.md)
  : Class Weights for Sample Weighting - Extended
- [`mlr_pipeops_colapply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_colapply.md)
  [`PipeOpColApply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_colapply.md)
  : Apply a Function to each Column of a Task
- [`mlr_pipeops_collapsefactors`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_collapsefactors.md)
  [`PipeOpCollapseFactors`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_collapsefactors.md)
  : Collapse Factors
- [`mlr_pipeops_colroles`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_colroles.md)
  [`PipeOpColRoles`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_colroles.md)
  : Change Column Roles of a Task
- [`mlr_pipeops_copy`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_copy.md)
  [`PipeOpCopy`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_copy.md)
  : Copy Input Multiple Times
- [`mlr_pipeops_datefeatures`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_datefeatures.md)
  [`PipeOpDateFeatures`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_datefeatures.md)
  : Preprocess Date Features
- [`mlr_pipeops_decode`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_decode.md)
  [`PipeOpDecode`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_decode.md)
  : Reverse Factor Encoding
- [`mlr_pipeops_encode`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encode.md)
  [`PipeOpEncode`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encode.md)
  : Factor Encoding
- [`mlr_pipeops_encodeimpact`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodeimpact.md)
  [`PipeOpEncodeImpact`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodeimpact.md)
  : Conditional Target Value Impact Encoding
- [`mlr_pipeops_encodelmer`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodelmer.md)
  [`PipeOpEncodeLmer`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodelmer.md)
  : Impact Encoding with Random Intercept Models
- [`mlr_pipeops_encodeplquantiles`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodeplquantiles.md)
  [`PipeOpEncodePLQuantiles`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodeplquantiles.md)
  : Piecewise Linear Encoding using Quantiles
- [`mlr_pipeops_encodepltree`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodepltree.md)
  [`PipeOpEncodePLTree`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_encodepltree.md)
  : Piecewise Linear Encoding using Decision Trees
- [`mlr_pipeops_featureunion`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_featureunion.md)
  [`PipeOpFeatureUnion`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_featureunion.md)
  : Aggregate Features from Multiple Inputs
- [`mlr_pipeops_filter`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_filter.md)
  [`PipeOpFilter`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_filter.md)
  : Feature Filtering
- [`mlr_pipeops_fixfactors`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_fixfactors.md)
  [`PipeOpFixFactors`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_fixfactors.md)
  : Fix Factor Levels
- [`mlr_pipeops_histbin`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_histbin.md)
  [`PipeOpHistBin`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_histbin.md)
  : Split Numeric Features into Equally Spaced Bins
- [`mlr_pipeops_ica`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_ica.md)
  [`PipeOpICA`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_ica.md)
  : Independent Component Analysis
- [`mlr_pipeops_imputeconstant`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputeconstant.md)
  [`PipeOpImputeConstant`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputeconstant.md)
  : Impute Features by a Constant
- [`mlr_pipeops_imputehist`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputehist.md)
  [`PipeOpImputeHist`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputehist.md)
  : Impute Numeric, Integer, POSIXct or Date Features by Histogram
- [`mlr_pipeops_imputelearner`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputelearner.md)
  [`PipeOpImputeLearner`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputelearner.md)
  : Impute Features by Fitting a Learner
- [`mlr_pipeops_imputemean`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputemean.md)
  [`PipeOpImputeMean`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputemean.md)
  : Impute Numeric, Integer, POSIXct or Date Features by their Mean
- [`mlr_pipeops_imputemedian`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputemedian.md)
  [`PipeOpImputeMedian`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputemedian.md)
  : Impute Numeric, Integer, POSIXct or Date Features by their Median
- [`mlr_pipeops_imputemode`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputemode.md)
  [`PipeOpImputeMode`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputemode.md)
  : Impute Features by their Mode
- [`mlr_pipeops_imputeoor`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputeoor.md)
  [`PipeOpImputeOOR`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputeoor.md)
  : Out of Range Imputation
- [`mlr_pipeops_imputesample`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputesample.md)
  [`PipeOpImputeSample`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_imputesample.md)
  : Impute Features by Sampling
- [`mlr_pipeops_info`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_info.md)
  [`PipeOpInfo`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_info.md)
  : Customizable Information Printer
- [`mlr_pipeops_isomap`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_isomap.md)
  [`PipeOpIsomap`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_isomap.md)
  : Algorithm for Dimensionality Reduction
- [`mlr_pipeops_kernelpca`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_kernelpca.md)
  [`PipeOpKernelPCA`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_kernelpca.md)
  : Kernelized Principal Component Analysis
- [`mlr_pipeops_learner`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner.md)
  [`PipeOpLearner`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner.md)
  : Wrap a Learner into a PipeOp
- [`mlr_pipeops_learner_cv`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner_cv.md)
  [`PipeOpLearnerCV`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner_cv.md)
  : Wrap a Learner into a PipeOp with Cross-validated Predictions as
  Features
- [`mlr_pipeops_learner_pi_cvplus`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner_pi_cvplus.md)
  [`PipeOpLearnerPICVPlus`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner_pi_cvplus.md)
  : Wrap a Learner into a PipeOp with Cross-validation Plus Confidence
  Intervals as Predictions
- [`mlr_pipeops_learner_quantiles`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner_quantiles.md)
  [`PipeOpLearnerQuantiles`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_learner_quantiles.md)
  : Wrap a Learner into a PipeOp to predict multiple Quantiles
- [`mlr_pipeops_missind`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_missind.md)
  [`PipeOpMissInd`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_missind.md)
  : Add Missing Indicator Columns
- [`mlr_pipeops_modelmatrix`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_modelmatrix.md)
  [`PipeOpModelMatrix`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_modelmatrix.md)
  : Transform Columns by Constructing a Model Matrix
- [`mlr_pipeops_multiplicityexply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_multiplicityexply.md)
  [`PipeOpMultiplicityExply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_multiplicityexply.md)
  : Explicate a Multiplicity
- [`mlr_pipeops_multiplicityimply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_multiplicityimply.md)
  [`PipeOpMultiplicityImply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_multiplicityimply.md)
  : Implicate a Multiplicity
- [`mlr_pipeops_mutate`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_mutate.md)
  [`PipeOpMutate`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_mutate.md)
  : Add Features According to Expressions
- [`mlr_pipeops_nearmiss`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_nearmiss.md)
  [`PipeOpNearmiss`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_nearmiss.md)
  : Nearmiss Down-Sampling
- [`mlr_pipeops_nmf`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_nmf.md)
  [`PipeOpNMF`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_nmf.md)
  : Non-negative Matrix Factorization
- [`mlr_pipeops_nop`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_nop.md)
  [`PipeOpNOP`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_nop.md)
  : Simply Push Input Forward
- [`mlr_pipeops_ovrsplit`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_ovrsplit.md)
  [`PipeOpOVRSplit`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_ovrsplit.md)
  : Split a Classification Task into Binary Classification Tasks
- [`mlr_pipeops_ovrunite`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_ovrunite.md)
  [`PipeOpOVRUnite`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_ovrunite.md)
  : Unite Binary Classification Tasks
- [`mlr_pipeops_pca`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_pca.md)
  [`PipeOpPCA`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_pca.md)
  : Principal Component Analysis
- [`mlr_pipeops_proxy`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_proxy.md)
  [`PipeOpProxy`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_proxy.md)
  : Wrap another PipeOp or Graph as a Hyperparameter
- [`mlr_pipeops_quantilebin`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_quantilebin.md)
  [`PipeOpQuantileBin`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_quantilebin.md)
  : Split Numeric Features into Quantile Bins
- [`mlr_pipeops_randomprojection`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_randomprojection.md)
  [`PipeOpRandomProjection`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_randomprojection.md)
  : Project Numeric Features onto a Randomly Sampled Subspace
- [`mlr_pipeops_randomresponse`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_randomresponse.md)
  [`PipeOpRandomResponse`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_randomresponse.md)
  : Generate a Randomized Response Prediction
- [`mlr_pipeops_regravg`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_regravg.md)
  [`PipeOpRegrAvg`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_regravg.md)
  : Weighted Prediction Averaging
- [`mlr_pipeops_removeconstants`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_removeconstants.md)
  [`PipeOpRemoveConstants`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_removeconstants.md)
  : Remove Constant Features
- [`mlr_pipeops_renamecolumns`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_renamecolumns.md)
  [`PipeOpRenameColumns`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_renamecolumns.md)
  : Rename Columns
- [`mlr_pipeops_replicate`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_replicate.md)
  [`PipeOpReplicate`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_replicate.md)
  : Replicate the Input as a Multiplicity
- [`mlr_pipeops_rowapply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_rowapply.md)
  [`PipeOpRowApply`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_rowapply.md)
  : Apply a Function to each Row of a Task
- [`mlr_pipeops_scale`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_scale.md)
  [`PipeOpScale`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_scale.md)
  : Center and Scale Numeric Features
- [`mlr_pipeops_scalemaxabs`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_scalemaxabs.md)
  [`PipeOpScaleMaxAbs`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_scalemaxabs.md)
  : Scale Numeric Features with Respect to their Maximum Absolute Value
- [`mlr_pipeops_scalerange`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_scalerange.md)
  [`PipeOpScaleRange`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_scalerange.md)
  : Linearly Transform Numeric Features to Match Given Boundaries
- [`mlr_pipeops_select`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_select.md)
  [`PipeOpSelect`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_select.md)
  : Remove Features Depending on a Selector
- [`mlr_pipeops_smote`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_smote.md)
  [`PipeOpSmote`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_smote.md)
  : SMOTE Balancing
- [`mlr_pipeops_smotenc`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_smotenc.md)
  [`PipeOpSmoteNC`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_smotenc.md)
  : SMOTENC Balancing
- [`mlr_pipeops_spatialsign`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_spatialsign.md)
  [`PipeOpSpatialSign`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_spatialsign.md)
  : Normalize Data Row-wise
- [`mlr_pipeops_splines`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_splines.md)
  [`PipeOpSplines`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_splines.md)
  : Transforms Numeric Features into Spline Basis Expansions
- [`mlr_pipeops_subsample`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_subsample.md)
  [`PipeOpSubsample`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_subsample.md)
  : Subsampling
- [`mlr_pipeops_targetinvert`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_targetinvert.md)
  [`PipeOpTargetInvert`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_targetinvert.md)
  : Invert Target Transformations
- [`mlr_pipeops_targetmutate`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_targetmutate.md)
  [`PipeOpTargetMutate`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_targetmutate.md)
  : Transform a Target by a Function
- [`mlr_pipeops_targettrafoscalerange`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_targettrafoscalerange.md)
  [`PipeOpTargetTrafoScaleRange`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_targettrafoscalerange.md)
  : Linearly Transform a Numeric Target to Match Given Boundaries
- [`mlr_pipeops_textvectorizer`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_textvectorizer.md)
  [`PipeOpTextVectorizer`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_textvectorizer.md)
  : Bag-of-word Representation of Character Features
- [`mlr_pipeops_threshold`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_threshold.md)
  [`PipeOpThreshold`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_threshold.md)
  : Change the Threshold of a Classification Prediction
- [`mlr_pipeops_tomek`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_tomek.md)
  [`PipeOpTomek`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_tomek.md)
  : Tomek Down-Sampling
- [`mlr_pipeops_tunethreshold`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_tunethreshold.md)
  [`PipeOpTuneThreshold`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_tunethreshold.md)
  : Tune the Threshold of a Classification Prediction
- [`mlr_pipeops_unbranch`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_unbranch.md)
  [`PipeOpUnbranch`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_unbranch.md)
  : Unbranch Different Paths
- [`mlr_pipeops_updatetarget`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_updatetarget.md)
  [`PipeOpUpdateTarget`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_updatetarget.md)
  : Transform a Target without an Explicit Inversion
- [`mlr_pipeops_vtreat`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_vtreat.md)
  [`PipeOpVtreat`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_vtreat.md)
  : Interface to the vtreat Package
- [`mlr_pipeops_yeojohnson`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_yeojohnson.md)
  [`PipeOpYeoJohnson`](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_yeojohnson.md)
  : Yeo-Johnson Transformation of Numeric Features
- [`PipeOp`](https://mlr3pipelines.mlr-org.com/reference/PipeOp.md) :
  PipeOp Base Class
- [`PipeOpEncodePL`](https://mlr3pipelines.mlr-org.com/reference/PipeOpEncodePL.md)
  : Piecewise Linear Encoding Base Class
- [`PipeOpEnsemble`](https://mlr3pipelines.mlr-org.com/reference/PipeOpEnsemble.md)
  : Ensembling Base Class
- [`PipeOpImpute`](https://mlr3pipelines.mlr-org.com/reference/PipeOpImpute.md)
  : Imputation Base Class
- [`PipeOpTargetTrafo`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTargetTrafo.md)
  : Target Transformation Base Class
- [`PipeOpTaskPreproc`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTaskPreproc.md)
  : Task Preprocessing Base Class
- [`PipeOpTaskPreprocSimple`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTaskPreprocSimple.md)
  : Simple Task Preprocessing Base Class

## Pipelines

- [`mlr_graphs`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs.md)
  : Dictionary of (sub-)graphs
- [`ppl()`](https://mlr3pipelines.mlr-org.com/reference/ppl.md)
  [`ppls()`](https://mlr3pipelines.mlr-org.com/reference/ppl.md) :
  Shorthand Graph Constructor
- [`pipeline_bagging()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_bagging.md)
  : Create a bagging learner
- [`pipeline_branch()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_branch.md)
  : Branch Between Alternative Paths
- [`pipeline_convert_types()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_convert_types.md)
  : Convert Column Types
- [`pipeline_greplicate()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_greplicate.md)
  : Create Disjoint Graph Union of Copies of a Graph
- [`pipeline_ovr()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_ovr.md)
  : Create A Graph to Perform "One vs. Rest" classification.
- [`pipeline_robustify()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_robustify.md)
  : Robustify a learner
- [`pipeline_stacking()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_stacking.md)
  : Create A Graph to Perform Stacking.
- [`pipeline_targettrafo()`](https://mlr3pipelines.mlr-org.com/reference/mlr_graphs_targettrafo.md)
  : Transform and Re-Transform the Target Variable

## Learners

- [`mlr_learners_graph`](https://mlr3pipelines.mlr-org.com/reference/mlr_learners_graph.md)
  [`GraphLearner`](https://mlr3pipelines.mlr-org.com/reference/mlr_learners_graph.md)
  : Encapsulate a Graph as a Learner
- [`mlr_learners_classif.avg`](https://mlr3pipelines.mlr-org.com/reference/mlr_learners_avg.md)
  [`mlr_learners_regr.avg`](https://mlr3pipelines.mlr-org.com/reference/mlr_learners_avg.md)
  : Optimized Weighted Average of Features for Classification and
  Regression

## Tasks

- [`mlr_tasks_boston_housing`](https://mlr3pipelines.mlr-org.com/reference/mlr_tasks_boston_housing.md)
  : Housing Data for 506 Census Tracts of Boston

## Filters

- [`mlr_filters_ensemble`](https://mlr3pipelines.mlr-org.com/reference/mlr_filters_ensemble.md)
  [`FilterEnsemble`](https://mlr3pipelines.mlr-org.com/reference/mlr_filters_ensemble.md)
  : Filter Ensemble

## Helpers

- [`preproc()`](https://mlr3pipelines.mlr-org.com/reference/preproc.md)
  : Simple Pre-processing
- [`selector_all()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_none()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_type()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_grep()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_name()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_invert()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_intersect()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_union()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_setdiff()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_missing()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  [`selector_cardinality_greater_than()`](https://mlr3pipelines.mlr-org.com/reference/Selector.md)
  : Selector Functions
- [`as_graph()`](https://mlr3pipelines.mlr-org.com/reference/as_graph.md)
  : Conversion to mlr3pipelines Graph
- [`assert_graph()`](https://mlr3pipelines.mlr-org.com/reference/assert_graph.md)
  : Assertion for mlr3pipelines Graph
- [`as_pipeop()`](https://mlr3pipelines.mlr-org.com/reference/as_pipeop.md)
  : Conversion to mlr3pipelines PipeOp
- [`assert_pipeop()`](https://mlr3pipelines.mlr-org.com/reference/assert_pipeop.md)
  : Assertion for mlr3pipelines PipeOp
- [`is_noop()`](https://mlr3pipelines.mlr-org.com/reference/is_noop.md)
  : Test for NO_OP
- [`NO_OP`](https://mlr3pipelines.mlr-org.com/reference/NO_OP.md) :
  No-Op Sentinel Used for Alternative Branching
- [`filter_noop()`](https://mlr3pipelines.mlr-org.com/reference/filter_noop.md)
  : Remove NO_OPs from a List
- [`set_validate(`*`<GraphLearner>`*`)`](https://mlr3pipelines.mlr-org.com/reference/set_validate.GraphLearner.md)
  : Configure Validation for a GraphLearner
- [`as.Multiplicity()`](https://mlr3pipelines.mlr-org.com/reference/as.Multiplicity.md)
  : Convert an object to a Multiplicity
- [`is.Multiplicity()`](https://mlr3pipelines.mlr-org.com/reference/is.Multiplicity.md)
  : Check if an object is a Multiplicity

## Abstract PipeOps

- [`PipeOp`](https://mlr3pipelines.mlr-org.com/reference/PipeOp.md) :
  PipeOp Base Class
- [`PipeOpTaskPreproc`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTaskPreproc.md)
  : Task Preprocessing Base Class
- [`PipeOpTaskPreprocSimple`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTaskPreprocSimple.md)
  : Simple Task Preprocessing Base Class
- [`PipeOpImpute`](https://mlr3pipelines.mlr-org.com/reference/PipeOpImpute.md)
  : Imputation Base Class
- [`PipeOpEnsemble`](https://mlr3pipelines.mlr-org.com/reference/PipeOpEnsemble.md)
  : Ensembling Base Class
- [`PipeOpTargetTrafo`](https://mlr3pipelines.mlr-org.com/reference/PipeOpTargetTrafo.md)
  : Target Transformation Base Class
- [`PipeOpEncodePL`](https://mlr3pipelines.mlr-org.com/reference/PipeOpEncodePL.md)
  : Piecewise Linear Encoding Base Class

## PipeOp Type Inference

- [`add_class_hierarchy_cache()`](https://mlr3pipelines.mlr-org.com/reference/add_class_hierarchy_cache.md)
  : Add a Class Hierarchy to the Cache
- [`reset_class_hierarchy_cache()`](https://mlr3pipelines.mlr-org.com/reference/reset_class_hierarchy_cache.md)
  : Reset the Class Hierarchy Cache
- [`register_autoconvert_function()`](https://mlr3pipelines.mlr-org.com/reference/register_autoconvert_function.md)
  : Add Autoconvert Function to Conversion Register
- [`reset_autoconvert_register()`](https://mlr3pipelines.mlr-org.com/reference/reset_autoconvert_register.md)
  : Reset Autoconvert Register

# Articles

### All vignettes

- [Adding new
  PipeOps](https://mlr3pipelines.mlr-org.com/articles/extending.md):