| mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3' | mlr3pipelines-package mlr3pipelines |
| PipeOp Composition Operator | %>>!% %>>% concat_graphs |
| Add a Class Hierarchy to the Cache | add_class_hierarchy_cache |
| Conversion to mlr3pipelines Graph | as_graph |
| Conversion to mlr3pipelines PipeOp | as_pipeop |
| Convert an object to a Multiplicity | as.Multiplicity |
| Assertion for mlr3pipelines Graph | assert_graph |
| Assertion for mlr3pipelines PipeOp | assert_pipeop |
| Chain a Series of Graphs | chain_graphs |
| Remove NO_OPs from a List | filter_noop |
| Graph Base Class | Graph |
| Create Disjoint Graph Union of Copies of a Graph | greplicate |
| Disjoint Union of Graphs | gunion |
| Test for NO_OP | is_noop |
| Check if an object is a Multiplicity | is.Multiplicity |
| Dictionary of (sub-)graphs | mlr_graphs |
| Create a bagging learner | mlr_graphs_bagging pipeline_bagging |
| Branch Between Alternative Paths | mlr_graphs_branch pipeline_branch |
| Convert Column Types | mlr_graphs_convert_types pipeline_convert_types |
| Create Disjoint Graph Union of Copies of a Graph | mlr_graphs_greplicate pipeline_greplicate |
| Create A Graph to Perform "One vs. Rest" classification. | mlr_graphs_ovr pipeline_ovr |
| Robustify a learner | mlr_graphs_robustify pipeline_robustify |
| Create A Graph to Perform Stacking. | mlr_graphs_stacking pipeline_stacking |
| Transform and Re-Transform the Target Variable | mlr_graphs_targettrafo pipeline_targettrafo |
| Optimized Weighted Average of Features for Classification and Regression | LearnerClassifAvg LearnerRegrAvg mlr_learners_avg mlr_learners_classif.avg mlr_learners_regr.avg |
| Encapsulate a Graph as a Learner | GraphLearner mlr_learners_graph |
| Dictionary of PipeOps | mlr_pipeops |
| Box-Cox Transformation of Numeric Features | mlr_pipeops_boxcox PipeOpBoxCox |
| Path Branching | mlr_pipeops_branch PipeOpBranch |
| Chunk Input into Multiple Outputs | mlr_pipeops_chunk PipeOpChunk |
| Class Balancing | mlr_pipeops_classbalancing PipeOpClassBalancing |
| Majority Vote Prediction | mlr_pipeops_classifavg PipeOpClassifAvg |
| Class Weights for Sample Weighting | mlr_pipeops_classweights PipeOpClassWeights |
| Apply a Function to each Column of a Task | mlr_pipeops_colapply PipeOpColApply |
| Collapse Factors | mlr_pipeops_collapsefactors PipeOpCollapseFactors |
| Change Column Roles of a Task | mlr_pipeops_colroles PipeOpColRoles |
| Copy Input Multiple Times | mlr_pipeops_copy PipeOpCopy |
| Preprocess Date Features | mlr_pipeops_datefeatures PipeOpDateFeatures |
| Factor Encoding | mlr_pipeops_encode PipeOpEncode |
| Conditional Target Value Impact Encoding | mlr_pipeops_encodeimpact PipeOpEncodeImpact |
| Impact Encoding with Random Intercept Models | mlr_pipeops_encodelmer PipeOpEncodeLmer |
| Aggregate Features from Multiple Inputs | mlr_pipeops_featureunion PipeOpFeatureUnion |
| Feature Filtering | mlr_pipeops_filter PipeOpFilter |
| Fix Factor Levels | mlr_pipeops_fixfactors PipeOpFixFactors |
| Split Numeric Features into Equally Spaced Bins | mlr_pipeops_histbin PipeOpHistBin |
| Independent Component Analysis | mlr_pipeops_ica PipeOpICA |
| Impute Features by a Constant | mlr_pipeops_imputeconstant PipeOpImputeConstant |
| Impute Numerical Features by Histogram | mlr_pipeops_imputehist PipeOpImputeHist |
| Impute Features by Fitting a Learner | mlr_pipeops_imputelearner PipeOpImputeLearner |
| Impute Numerical Features by their Mean | mlr_pipeops_imputemean PipeOpImputeMean |
| Impute Numerical Features by their Median | mlr_pipeops_imputemedian PipeOpImputeMedian |
| Impute Features by their Mode | mlr_pipeops_imputemode PipeOpImputeMode |
| Out of Range Imputation | mlr_pipeops_imputeoor PipeOpImputeOOR |
| Impute Features by Sampling | mlr_pipeops_imputesample PipeOpImputeSample |
| Kernelized Principle Component Analysis | mlr_pipeops_kernelpca PipeOpKernelPCA |
| Wrap a Learner into a PipeOp | mlr_pipeops_learner PipeOpLearner |
| Wrap a Learner into a PipeOp with Cross-validated Predictions as Features | mlr_pipeops_learner_cv PipeOpLearnerCV |
| Add Missing Indicator Columns | mlr_pipeops_missind PipeOpMissInd |
| Transform Columns by Constructing a Model Matrix | mlr_pipeops_modelmatrix PipeOpModelMatrix |
| Explicate a Multiplicity | mlr_pipeops_multiplicityexply PipeOpMultiplicityExply |
| Implicate a Multiplicity | mlr_pipeops_multiplicityimply PipeOpMultiplicityImply |
| Add Features According to Expressions | mlr_pipeops_mutate PipeOpMutate |
| Non-negative Matrix Factorization | mlr_pipeops_nmf PipeOpNMF |
| Simply Push Input Forward | mlr_pipeops_nop PipeOpNOP |
| Split a Classification Task into Binary Classification Tasks | mlr_pipeops_ovrsplit PipeOpOVRSplit |
| Unite Binary Classification Tasks | mlr_pipeops_ovrunite PipeOpOVRUnite |
| Principle Component Analysis | mlr_pipeops_pca PipeOpPCA |
| Wrap another PipeOp or Graph as a Hyperparameter | mlr_pipeops_proxy PipeOpProxy |
| Split Numeric Features into Quantile Bins | mlr_pipeops_quantilebin PipeOpQuantileBin |
| Project Numeric Features onto a Randomly Sampled Subspace | mlr_pipeops_randomprojection PipeOpRandomProjection |
| Generate a Randomized Response Prediction | mlr_pipeops_randomresponse PipeOpRandomResponse |
| Weighted Prediction Averaging | mlr_pipeops_regravg PipeOpRegrAvg |
| Remove Constant Features | mlr_pipeops_removeconstants PipeOpRemoveConstants |
| Rename Columns | mlr_pipeops_renamecolumns PipeOpRenameColumns |
| Replicate the Input as a Multiplicity | mlr_pipeops_replicate PipeOpReplicate |
| Center and Scale Numeric Features | mlr_pipeops_scale PipeOpScale |
| Scale Numeric Features with Respect to their Maximum Absolute Value | mlr_pipeops_scalemaxabs PipeOpScaleMaxAbs |
| Linearly Transform Numeric Features to Match Given Boundaries | mlr_pipeops_scalerange PipeOpScaleRange |
| Remove Features Depending on a Selector | mlr_pipeops_select PipeOpSelect |
| SMOTE Balancing | mlr_pipeops_smote PipeOpSmote |
| Normalize Data Row-wise | mlr_pipeops_spatialsign PipeOpSpatialSign |
| Subsampling | mlr_pipeops_subsample PipeOpSubsample |
| Invert Target Transformations | mlr_pipeops_targetinvert PipeOpTargetInvert |
| Transform a Target by a Function | mlr_pipeops_targetmutate PipeOpTargetMutate |
| Linearly Transform a Numeric Target to Match Given Boundaries | mlr_pipeops_targettrafoscalerange PipeOpTargetTrafoScaleRange |
| Bag-of-word Representation of Character Features | mlr_pipeops_textvectorizer PipeOpTextVectorizer |
| Change the Threshold of a Classification Prediction | mlr_pipeops_threshold PipeOpThreshold |
| Tune the Threshold of a Classification Prediction | mlr_pipeops_tunethreshold PipeOpTuneThreshold |
| Unbranch Different Paths | mlr_pipeops_unbranch PipeOpUnbranch |
| Transform a Target without an Explicit Inversion | mlr_pipeops_updatetarget PipeOpUpdateTarget |
| Interface to the vtreat Package | mlr_pipeops_vtreat PipeOpVtreat |
| Yeo-Johnson Transformation of Numeric Features | mlr_pipeops_yeojohnson PipeOpYeoJohnson |
| Multiplicity | Multiplicity |
| No-Op Sentinel Used for Alternative Branching | NO_OP |
| PipeOp Base Class | PipeOp |
| Ensembling Base Class | PipeOpEnsemble |
| Imputation Base Class | PipeOpImpute |
| Target Transformation Base Class | PipeOpTargetTrafo |
| Task Preprocessing Base Class | PipeOpTaskPreproc |
| Simple Task Preprocessing Base Class | PipeOpTaskPreprocSimple |
| Shorthand PipeOp Constructor | po pos |
| Shorthand Graph Constructor | ppl ppls |
| Add Autoconvert Function to Conversion Register | register_autoconvert_function |
| Reset Autoconvert Register | reset_autoconvert_register |
| Reset the Class Hierarchy Cache | reset_class_hierarchy_cache |
| Selector Functions | Selector selector_all selector_cardinality_greater_than selector_grep selector_intersect selector_invert selector_missing selector_name selector_none selector_setdiff selector_type selector_union |
| Configure Validation for a GraphLearner | set_validate.GraphLearner |
