%>>%                    PipeOp Composition Operator
Graph                   Graph Base Class
Multiplicity            Multiplicity
NO_OP                   No-Op Sentinel Used for Alternative Branching
PipeOp                  PipeOp Base Class
PipeOpEncodePL          Piecewise Linear Encoding 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
Selector                Selector Functions
add_class_hierarchy_cache
                        Add a Class Hierarchy to the Cache
as.Multiplicity         Convert an object to a Multiplicity
as_graph                Conversion to mlr3pipelines Graph
as_pipeop               Conversion to mlr3pipelines PipeOp
assert_graph            Assertion for mlr3pipelines Graph
assert_pipeop           Assertion for mlr3pipelines PipeOp
chain_graphs            Chain a Series of Graphs
filter_noop             Remove NO_OPs from a List
greplicate              Create Disjoint Graph Union of Copies of a
                        Graph
gunion                  Disjoint Union of Graphs
is.Multiplicity         Check if an object is a Multiplicity
is_noop                 Test for NO_OP
mlr3pipelines-package   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_graph      Encapsulate a Graph as a Learner
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_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 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_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_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
po                      Shorthand PipeOp Constructor
ppl                     Shorthand Graph Constructor
preproc                 Simple Pre-processing
register_autoconvert_function
                        Add Autoconvert Function to Conversion Register
reset_autoconvert_register
                        Reset Autoconvert Register
reset_class_hierarchy_cache
                        Reset the Class Hierarchy Cache
set_validate.GraphLearner
                        Configure Validation for a GraphLearner
