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