# Preprocessing Operators and Pipelines for 'mlr3'

## Documentation for package ‘mlr3pipelines’ version 0.11.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C F G I L M N P R S misc

mlr3pipelines-package | mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

## -- A --

add_class_hierarchy_cache | Add a Class Hierarchy to the Cache
as.Multiplicity | Convert an object to a Multiplicity
assert_graph | Assertion for mlr3pipelines Graph
assert_pipeop | Assertion for mlr3pipelines PipeOp
as_graph | Conversion to mlr3pipelines Graph
as_pipeop | Conversion to mlr3pipelines PipeOp

## -- C --

chain_graphs | Chain a Series of Graphs
concat_graphs | PipeOp Composition Operator

## -- F --

FilterEnsemble | Filter Ensemble
filter_noop | Remove NO_OPs from a List

## -- G --

Graph | Graph Base Class
GraphLearner | Encapsulate a Graph as a Learner
greplicate | Create Disjoint Graph Union of Copies of a Graph
gunion | Disjoint Union of Graphs

## -- I --

is.Multiplicity | Check if an object is a Multiplicity
is_noop | Test for NO_OP

## -- L --

LearnerClassifAvg | Optimized Weighted Average of Features for Classification and Regression
LearnerRegrAvg | Optimized Weighted Average of Features for Classification and Regression

## -- M --

mlr3pipelines | mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'
mlr_filters_ensemble | Filter Ensemble
mlr_graphs | Dictionary of (sub-)graphs
mlr_graphs_bagging | Create a bagging learner
mlr_graphs_branch | Branch Between Alternative Paths
mlr_graphs_convert_types | Convert Column Types
mlr_graphs_greplicate | Create Disjoint Graph Union of Copies of a Graph
mlr_graphs_ovr | Create A Graph to Perform "One vs. Rest" classification.
mlr_graphs_robustify | Robustify a learner
mlr_graphs_stacking | Create A Graph to Perform Stacking.
mlr_graphs_targettrafo | Transform and Re-Transform the Target Variable
mlr_learners_avg | Optimized Weighted Average of Features for Classification and Regression
mlr_learners_classif.avg | Optimized Weighted Average of Features for Classification and Regression
mlr_learners_graph | Encapsulate a Graph as a Learner
mlr_learners_regr.avg | Optimized Weighted Average of Features for Classification and Regression
mlr_pipeops | Dictionary of PipeOps
mlr_pipeops_adas | ADAS Balancing
mlr_pipeops_blsmote | BLSMOTE Balancing
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_classweightsex | Class Weights for Sample Weighting - Extended
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_decode | Reverse Factor Encoding
mlr_pipeops_encode | Factor Encoding
mlr_pipeops_encodeimpact | Conditional Target Value Impact Encoding
mlr_pipeops_encodelmer | Impact Encoding with Random Intercept Models
mlr_pipeops_encodeplquantiles | Piecewise Linear Encoding using Quantiles
mlr_pipeops_encodepltree | Piecewise Linear Encoding using Decision Trees
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 Numeric, Integer, POSIXct or Date Features by Histogram
mlr_pipeops_imputelearner | Impute Features by Fitting a Learner
mlr_pipeops_imputemean | Impute Numeric, Integer, POSIXct or Date Features by their Mean
mlr_pipeops_imputemedian | Impute Numeric, Integer, POSIXct or Date 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_info | Customizable Information Printer
mlr_pipeops_isomap | Algorithm for Dimensionality Reduction
mlr_pipeops_kernelpca | Kernelized Principal 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_learner_pi_cvplus | Wrap a Learner into a PipeOp with Cross-validation Plus Confidence Intervals as Predictions
mlr_pipeops_learner_quantiles | Wrap a Learner into a PipeOp to predict multiple Quantiles
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_nearmiss | Nearmiss Down-Sampling
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 | Principal 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_rowapply | Apply a Function to each Row of a Task
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_smotenc | SMOTENC Balancing
mlr_pipeops_spatialsign | Normalize Data Row-wise
mlr_pipeops_splines | Transforms Numeric Features into Spline Basis Expansions
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_tomek | Tomek Down-Sampling
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
mlr_tasks_boston_housing | Housing Data for 506 Census Tracts of Boston
Multiplicity | Multiplicity

## -- N --

NO_OP | No-Op Sentinel Used for Alternative Branching

## -- P --

pipeline_bagging | Create a bagging learner
pipeline_branch | Branch Between Alternative Paths
pipeline_convert_types | Convert Column Types
pipeline_greplicate | Create Disjoint Graph Union of Copies of a Graph
pipeline_ovr | Create A Graph to Perform "One vs. Rest" classification.
pipeline_robustify | Robustify a learner
pipeline_stacking | Create A Graph to Perform Stacking.
pipeline_targettrafo | Transform and Re-Transform the Target Variable
PipeOp | PipeOp Base Class
PipeOpADAS | ADAS Balancing
PipeOpBLSmote | BLSMOTE Balancing
PipeOpBoxCox | Box-Cox Transformation of Numeric Features
PipeOpBranch | Path Branching
PipeOpChunk | Chunk Input into Multiple Outputs
PipeOpClassBalancing | Class Balancing
PipeOpClassifAvg | Majority Vote Prediction
PipeOpClassWeights | Class Weights for Sample Weighting
PipeOpClassWeightsEx | Class Weights for Sample Weighting - Extended
PipeOpColApply | Apply a Function to each Column of a Task
PipeOpCollapseFactors | Collapse Factors
PipeOpColRoles | Change Column Roles of a Task
PipeOpCopy | Copy Input Multiple Times
PipeOpDateFeatures | Preprocess Date Features
PipeOpDecode | Reverse Factor Encoding
PipeOpEncode | Factor Encoding
PipeOpEncodeImpact | Conditional Target Value Impact Encoding
PipeOpEncodeLmer | Impact Encoding with Random Intercept Models
PipeOpEncodePL | Piecewise Linear Encoding Base Class
PipeOpEncodePLQuantiles | Piecewise Linear Encoding using Quantiles
PipeOpEncodePLTree | Piecewise Linear Encoding using Decision Trees
PipeOpEnsemble | Ensembling Base Class
PipeOpFeatureUnion | Aggregate Features from Multiple Inputs
PipeOpFilter | Feature Filtering
PipeOpFixFactors | Fix Factor Levels
PipeOpHistBin | Split Numeric Features into Equally Spaced Bins
PipeOpICA | Independent Component Analysis
PipeOpImpute | Imputation Base Class
PipeOpImputeConstant | Impute Features by a Constant
PipeOpImputeHist | Impute Numeric, Integer, POSIXct or Date Features by Histogram
PipeOpImputeLearner | Impute Features by Fitting a Learner
PipeOpImputeMean | Impute Numeric, Integer, POSIXct or Date Features by their Mean
PipeOpImputeMedian | Impute Numeric, Integer, POSIXct or Date Features by their Median
PipeOpImputeMode | Impute Features by their Mode
PipeOpImputeOOR | Out of Range Imputation
PipeOpImputeSample | Impute Features by Sampling
PipeOpInfo | Customizable Information Printer
PipeOpIsomap | Algorithm for Dimensionality Reduction
PipeOpKernelPCA | Kernelized Principal Component Analysis
PipeOpLearner | Wrap a Learner into a PipeOp
PipeOpLearnerCV | Wrap a Learner into a PipeOp with Cross-validated Predictions as Features
PipeOpLearnerPICVPlus | Wrap a Learner into a PipeOp with Cross-validation Plus Confidence Intervals as Predictions
PipeOpLearnerQuantiles | Wrap a Learner into a PipeOp to predict multiple Quantiles
PipeOpMissInd | Add Missing Indicator Columns
PipeOpModelMatrix | Transform Columns by Constructing a Model Matrix
PipeOpMultiplicityExply | Explicate a Multiplicity
PipeOpMultiplicityImply | Implicate a Multiplicity
PipeOpMutate | Add Features According to Expressions
PipeOpNearmiss | Nearmiss Down-Sampling
PipeOpNMF | Non-negative Matrix Factorization
PipeOpNOP | Simply Push Input Forward
PipeOpOVRSplit | Split a Classification Task into Binary Classification Tasks
PipeOpOVRUnite | Unite Binary Classification Tasks
PipeOpPCA | Principal Component Analysis
PipeOpProxy | Wrap another PipeOp or Graph as a Hyperparameter
PipeOpQuantileBin | Split Numeric Features into Quantile Bins
PipeOpRandomProjection | Project Numeric Features onto a Randomly Sampled Subspace
PipeOpRandomResponse | Generate a Randomized Response Prediction
PipeOpRegrAvg | Weighted Prediction Averaging
PipeOpRemoveConstants | Remove Constant Features
PipeOpRenameColumns | Rename Columns
PipeOpReplicate | Replicate the Input as a Multiplicity
PipeOpRowApply | Apply a Function to each Row of a Task
PipeOpScale | Center and Scale Numeric Features
PipeOpScaleMaxAbs | Scale Numeric Features with Respect to their Maximum Absolute Value
PipeOpScaleRange | Linearly Transform Numeric Features to Match Given Boundaries
PipeOpSelect | Remove Features Depending on a Selector
PipeOpSmote | SMOTE Balancing
PipeOpSmoteNC | SMOTENC Balancing
PipeOpSpatialSign | Normalize Data Row-wise
PipeOpSplines | Transforms Numeric Features into Spline Basis Expansions
PipeOpSubsample | Subsampling
PipeOpTargetInvert | Invert Target Transformations
PipeOpTargetMutate | Transform a Target by a Function
PipeOpTargetTrafo | Target Transformation Base Class
PipeOpTargetTrafoScaleRange | Linearly Transform a Numeric Target to Match Given Boundaries
PipeOpTaskPreproc | Task Preprocessing Base Class
PipeOpTaskPreprocSimple | Simple Task Preprocessing Base Class
PipeOpTextVectorizer | Bag-of-word Representation of Character Features
PipeOpThreshold | Change the Threshold of a Classification Prediction
PipeOpTomek | Tomek Down-Sampling
PipeOpTuneThreshold | Tune the Threshold of a Classification Prediction
PipeOpUnbranch | Unbranch Different Paths
PipeOpUpdateTarget | Transform a Target without an Explicit Inversion
PipeOpVtreat | Interface to the vtreat Package
PipeOpYeoJohnson | Yeo-Johnson Transformation of Numeric Features
po | Shorthand PipeOp Constructor
pos | Shorthand PipeOp Constructor
ppl | Shorthand Graph Constructor
ppls | Shorthand Graph Constructor
preproc | Simple Pre-processing

## -- R --

register_autoconvert_function | Add Autoconvert Function to Conversion Register
reset_autoconvert_register | Reset Autoconvert Register
reset_class_hierarchy_cache | Reset the Class Hierarchy Cache

## -- S --

Selector | Selector Functions
selector_all | Selector Functions
selector_cardinality_greater_than | Selector Functions
selector_grep | Selector Functions
selector_intersect | Selector Functions
selector_invert | Selector Functions
selector_missing | Selector Functions
selector_name | Selector Functions
selector_none | Selector Functions
selector_setdiff | Selector Functions
selector_type | Selector Functions
selector_union | Selector Functions
set_validate.GraphLearner | Configure Validation for a GraphLearner

## -- misc --

%>>!% | PipeOp Composition Operator
%>>% | PipeOp Composition Operator