Function reference

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
 BoxCox 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 Crossvalidated 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
 Nonnegative 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 Rowwise

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
 Bagofword 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
 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_robustify()
 Robustify a learner

pipeline_targettrafo()
 Transform and ReTransform the Target Variable

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

pipeline_ovr()
 Create A Graph to Perform "One vs. Rest" classification.

pipeline_stacking()
 Create A Graph to Perform Stacking.

mlr_graphs
 Dictionary of (sub)graphs

pipeline_bagging()
 Create a bagging learner

pipeline_branch()
 Branch Between Alternative Paths

pipeline_robustify()
 Robustify a learner

pipeline_targettrafo()
 Transform and ReTransform the Target Variable

chain_graphs()
 Chain a Series of Graphs

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

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