Function reference • mlr3pipelines

Package
`mlr3pipelines-package`	mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'
Building Blocks
`PipeOp`	PipeOp Base Class
`Graph`	Graph Base Class
`PipeOpTaskPreproc`	Task Preprocessing Base Class
`PipeOpTaskPreprocSimple`	Simple Task Preprocessing Base Class
`PipeOpImpute`	Imputation Base Class
Graph Tools
`%>>%`	PipeOp Composition Operator
`gunion()`	Disjoint Union of Graphs
`greplicate()`	Create Disjoint Graph Union of Copies of a Graph
PipeOps
`mlr_pipeops`	Dictionary of PipeOps
`po()`	Shorthand PipeOp Constructor
`mlr_pipeops_boxcox`	Box-Cox Transformation of Numeric Features
`mlr_pipeops_branch`	Path Branching
`mlr_pipeops_chunk`	Chunk Input into Multiple Outputs
`mlr_pipeops_classbalancing`	Class Balancing
`mlr_pipeops_classifavg`	Majority Vote Prediction
`mlr_pipeops_classweights`	Class Weights for Sample Weighting
`mlr_pipeops_colapply`	Apply a Function to each Column of a Task
`mlr_pipeops_collapsefactors`	Collapse Factors
`mlr_pipeops_colroles`	Change Column Roles of a Task
`mlr_pipeops_copy`	Copy Input Multiple Times
`mlr_pipeops_datefeatures`	Preprocess Date Features
`mlr_pipeops_encode`	Factor Encoding
`mlr_pipeops_encodeimpact`	Conditional Target Value Impact Encoding
`mlr_pipeops_encodelmer`	Impact Encoding with Random Intercept Models
`mlr_pipeops_featureunion`	Aggregate Features from Multiple Inputs
`mlr_pipeops_filter`	Feature Filtering
`mlr_pipeops_fixfactors`	Fix Factor Levels
`mlr_pipeops_histbin`	Split Numeric Features into Equally Spaced Bins
`mlr_pipeops_ica`	Independent Component Analysis
`mlr_pipeops_imputeconstant`	Impute Features by a Constant
`mlr_pipeops_imputehist`	Impute Numerical Features by Histogram
`mlr_pipeops_imputelearner`	Impute Features by Fitting a Learner
`mlr_pipeops_imputemean`	Impute Numerical Features by their Mean
`mlr_pipeops_imputemedian`	Impute Numerical Features by their Median
`mlr_pipeops_imputemode`	Impute Features by their Mode
`mlr_pipeops_imputeoor`	Out of Range Imputation
`mlr_pipeops_imputesample`	Impute Features by Sampling
`mlr_pipeops_kernelpca`	Kernelized Principle Component Analysis
`mlr_pipeops_learner`	Wrap a Learner into a PipeOp
`mlr_pipeops_learner_cv`	Wrap a Learner into a PipeOp with Cross-validated Predictions as Features
`mlr_pipeops_missind`	Add Missing Indicator Columns
`mlr_pipeops_modelmatrix`	Transform Columns by Constructing a Model Matrix
`mlr_pipeops_multiplicityexply`	Explicate a Multiplicity
`mlr_pipeops_multiplicityimply`	Implicate a Multiplicity
`mlr_pipeops_mutate`	Add Features According to Expressions
`mlr_pipeops_nmf`	Non-negative Matrix Factorization
`mlr_pipeops_nop`	Simply Push Input Forward
`mlr_pipeops_ovrsplit`	Split a Classification Task into Binary Classification Tasks
`mlr_pipeops_ovrunite`	Unite Binary Classification Tasks
`mlr_pipeops_pca`	Principle Component Analysis
`mlr_pipeops_proxy`	Wrap another PipeOp or Graph as a Hyperparameter
`mlr_pipeops_quantilebin`	Split Numeric Features into Quantile Bins
`mlr_pipeops_randomprojection`	Project Numeric Features onto a Randomly Sampled Subspace
`mlr_pipeops_randomresponse`	Generate a Randomized Response Prediction
`mlr_pipeops_regravg`	Weighted Prediction Averaging
`mlr_pipeops_removeconstants`	Remove Constant Features
`mlr_pipeops_renamecolumns`	Rename Columns
`mlr_pipeops_replicate`	Replicate the Input as a Multiplicity
`mlr_pipeops_scale`	Center and Scale Numeric Features
`mlr_pipeops_scalemaxabs`	Scale Numeric Features with Respect to their Maximum Absolute Value
`mlr_pipeops_scalerange`	Linearly Transform Numeric Features to Match Given Boundaries
`mlr_pipeops_select`	Remove Features Depending on a Selector
`mlr_pipeops_smote`	SMOTE Balancing
`mlr_pipeops_spatialsign`	Normalize Data Row-wise
`mlr_pipeops_subsample`	Subsampling
`mlr_pipeops_targetinvert`	Invert Target Transformations
`mlr_pipeops_targetmutate`	Transform a Target by a Function
`mlr_pipeops_targettrafoscalerange`	Linearly Transform a Numeric Target to Match Given Boundaries
`mlr_pipeops_textvectorizer`	Bag-of-word Representation of Character Features
`mlr_pipeops_threshold`	Change the Threshold of a Classification Prediction
`mlr_pipeops_tunethreshold`	Tune the Threshold of a Classification Prediction
`mlr_pipeops_unbranch`	Unbranch Different Paths
`mlr_pipeops_updatetarget`	Transform a Target without an Explicit Inversion
`mlr_pipeops_vtreat`	Interface to the vtreat Package
`mlr_pipeops_yeojohnson`	Yeo-Johnson Transformation of Numeric Features
Learners
`mlr_learners_graph`	Encapsulate a Graph as a Learner
`mlr_learners_classif.avg` `mlr_learners_regr.avg`	Optimized Weighted Average of Features for Classification and Regression
Helpers
`selector_all()` `selector_none()` `selector_type()` `selector_grep()` `selector_name()` `selector_invert()` `selector_intersect()` `selector_union()` `selector_setdiff()` `selector_missing()` `selector_cardinality_greater_than()`	Selector Functions
`as_graph()`	Conversion to mlr3pipelines Graph
`assert_graph()`	Assertion for mlr3pipelines Graph
`as_pipeop()`	Conversion to mlr3pipelines PipeOp
`assert_pipeop()`	Assertion for mlr3pipelines PipeOp
`is_noop()`	Test for NO_OP
`NO_OP`	No-Op Sentinel Used for Alternative Branching
`filter_noop()`	Remove NO_OPs from a List
Abstract PipeOps
`PipeOpEnsemble`	Ensembling Base Class
PipeOp Type Inference
`add_class_hierarchy_cache()`	Add a Class Hierarchy to the Cache
`reset_class_hierarchy_cache()`	Reset the Class Hierarchy Cache
`register_autoconvert_function()`	Add Autoconvert Function to Conversion Register
`reset_autoconvert_register()`	Reset Autoconvert Register

Reference

Package

Building Blocks

Graph Tools

PipeOps

Learners

Helpers

Abstract PipeOps

PipeOp Type Inference