Function reference

mlr3pipelines
mlr3pipelinespackage
 mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'

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

`%>>%`
concat_graphs()
`%>>!%`
 PipeOp Composition Operator

gunion()
 Disjoint Union of Graphs

greplicate()
 Create Disjoint Graph Union of Copies of a Graph

mlr_pipeops
 Dictionary of PipeOps

mlr_pipeops_boxcox
PipeOpBoxCox
 BoxCox Transformation of Numeric Features

mlr_pipeops_branch
PipeOpBranch
 Path Branching

mlr_pipeops_chunk
PipeOpChunk
 Chunk Input into Multiple Outputs

mlr_pipeops_classbalancing
PipeOpClassBalancing
 Class Balancing

mlr_pipeops_classifavg
PipeOpClassifAvg
 Majority Vote Prediction

mlr_pipeops_classweights
PipeOpClassWeights
 Class Weights for Sample Weighting

mlr_pipeops_colapply
PipeOpColApply
 Apply a Function to each Column of a Task

mlr_pipeops_collapsefactors
PipeOpCollapseFactors
 Collapse Factors

mlr_pipeops_colroles
PipeOpColRoles
 Change Column Roles of a Task

mlr_pipeops_copy
PipeOpCopy
 Copy Input Multiple Times

mlr_pipeops_datefeatures
PipeOpDateFeatures
 Preprocess Date Features

mlr_pipeops_encode
PipeOpEncode
 Factor Encoding

mlr_pipeops_encodeimpact
PipeOpEncodeImpact
 Conditional Target Value Impact Encoding

mlr_pipeops_encodelmer
PipeOpEncodeLmer
 Impact Encoding with Random Intercept Models

mlr_pipeops_featureunion
PipeOpFeatureUnion
 Aggregate Features from Multiple Inputs

mlr_pipeops_filter
PipeOpFilter
 Feature Filtering

mlr_pipeops_fixfactors
PipeOpFixFactors
 Fix Factor Levels

mlr_pipeops_histbin
PipeOpHistBin
 Split Numeric Features into Equally Spaced Bins

mlr_pipeops_ica
PipeOpICA
 Independent Component Analysis

mlr_pipeops_imputeconstant
PipeOpImputeConstant
 Impute Features by a Constant

mlr_pipeops_imputehist
PipeOpImputeHist
 Impute Numerical Features by Histogram

mlr_pipeops_imputelearner
PipeOpImputeLearner
 Impute Features by Fitting a Learner

mlr_pipeops_imputemean
PipeOpImputeMean
 Impute Numerical Features by their Mean

mlr_pipeops_imputemedian
PipeOpImputeMedian
 Impute Numerical Features by their Median

mlr_pipeops_imputemode
PipeOpImputeMode
 Impute Features by their Mode

mlr_pipeops_imputeoor
PipeOpImputeOOR
 Out of Range Imputation

mlr_pipeops_imputesample
PipeOpImputeSample
 Impute Features by Sampling

mlr_pipeops_kernelpca
PipeOpKernelPCA
 Kernelized Principle Component Analysis

mlr_pipeops_learner
PipeOpLearner
 Wrap a Learner into a PipeOp

mlr_pipeops_learner_cv
PipeOpLearnerCV
 Wrap a Learner into a PipeOp with Crossvalidated Predictions as Features

mlr_pipeops_missind
PipeOpMissInd
 Add Missing Indicator Columns

mlr_pipeops_modelmatrix
PipeOpModelMatrix
 Transform Columns by Constructing a Model Matrix

mlr_pipeops_multiplicityexply
PipeOpMultiplicityExply
 Explicate a Multiplicity

mlr_pipeops_multiplicityimply
PipeOpMultiplicityImply
 Implicate a Multiplicity

mlr_pipeops_mutate
PipeOpMutate
 Add Features According to Expressions

mlr_pipeops_nmf
PipeOpNMF
 Nonnegative Matrix Factorization

mlr_pipeops_nop
PipeOpNOP
 Simply Push Input Forward

mlr_pipeops_ovrsplit
PipeOpOVRSplit
 Split a Classification Task into Binary Classification Tasks

mlr_pipeops_ovrunite
PipeOpOVRUnite
 Unite Binary Classification Tasks

mlr_pipeops_pca
PipeOpPCA
 Principle Component Analysis

mlr_pipeops_proxy
PipeOpProxy
 Wrap another PipeOp or Graph as a Hyperparameter

mlr_pipeops_quantilebin
PipeOpQuantileBin
 Split Numeric Features into Quantile Bins

mlr_pipeops_randomprojection
PipeOpRandomProjection
 Project Numeric Features onto a Randomly Sampled Subspace

mlr_pipeops_randomresponse
PipeOpRandomResponse
 Generate a Randomized Response Prediction

mlr_pipeops_regravg
PipeOpRegrAvg
 Weighted Prediction Averaging

mlr_pipeops_removeconstants
PipeOpRemoveConstants
 Remove Constant Features

mlr_pipeops_renamecolumns
PipeOpRenameColumns
 Rename Columns

mlr_pipeops_replicate
PipeOpReplicate
 Replicate the Input as a Multiplicity

mlr_pipeops_scale
PipeOpScale
 Center and Scale Numeric Features

mlr_pipeops_scalemaxabs
PipeOpScaleMaxAbs
 Scale Numeric Features with Respect to their Maximum Absolute Value

mlr_pipeops_scalerange
PipeOpScaleRange
 Linearly Transform Numeric Features to Match Given Boundaries

mlr_pipeops_select
PipeOpSelect
 Remove Features Depending on a Selector

mlr_pipeops_smote
PipeOpSmote
 SMOTE Balancing

mlr_pipeops_spatialsign
PipeOpSpatialSign
 Normalize Data Rowwise

mlr_pipeops_subsample
PipeOpSubsample
 Subsampling

mlr_pipeops_targetinvert
PipeOpTargetInvert
 Invert Target Transformations

mlr_pipeops_targetmutate
PipeOpTargetMutate
 Transform a Target by a Function

mlr_pipeops_targettrafoscalerange
PipeOpTargetTrafoScaleRange
 Linearly Transform a Numeric Target to Match Given Boundaries

mlr_pipeops_textvectorizer
PipeOpTextVectorizer
 Bagofword Representation of Character Features

mlr_pipeops_threshold
PipeOpThreshold
 Change the Threshold of a Classification Prediction

mlr_pipeops_tunethreshold
PipeOpTuneThreshold
 Tune the Threshold of a Classification Prediction

mlr_pipeops_unbranch
PipeOpUnbranch
 Unbranch Different Paths

mlr_pipeops_updatetarget
PipeOpUpdateTarget
 Transform a Target without an Explicit Inversion

mlr_pipeops_vtreat
PipeOpVtreat
 Interface to the vtreat Package

mlr_pipeops_yeojohnson
PipeOpYeoJohnson
 YeoJohnson 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

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 ReTransform the Target Variable

mlr_graphs
 Dictionary of (sub)graphs

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 ReTransform the Target Variable

chain_graphs()
 Chain a Series of Graphs

mlr_learners_graph
GraphLearner
 Encapsulate a Graph as a Learner

mlr_learners_classif.avg
mlr_learners_regr.avg
 Optimized Weighted Average of Features for Classification and Regression

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
 NoOp 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

PipeOpEnsemble
 Ensembling Base Class

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