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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
Multiplicity()
Multiplicity

Graph Tools

`%>>%` concat_graphs() `%>>!%`
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() pos()
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
PipeOp
PipeOp Base Class
PipeOpEnsemble
Ensembling Base Class
PipeOpImpute
Imputation Base Class
PipeOpTargetTrafo
Target Transformation Base Class
PipeOpTaskPreproc
Task Preprocessing Base Class
PipeOpTaskPreprocSimple
Simple Task Preprocessing Base Class

Pipelines

ppl() ppls()
Shorthand Graph Constructor
pipeline_bagging()
Create a bagging learner
pipeline_branch()
Branch Between Alternative Paths
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

Graphs

mlr_graphs
Dictionary of (sub-)graphs
pipeline_bagging()
Create a bagging learner
pipeline_branch()
Branch Between Alternative Paths
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
chain_graphs()
Chain a Series of Graphs

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
as.Multiplicity()
Convert an object to a Multiplicity
is.Multiplicity()
Check if an object is a Multiplicity

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