Preprocessing Operators and Pipelines for 'mlr3'
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Documentation for package ‘mlr3pipelines’ version 0.6.0
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| mlr3pipelines-package | mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3' |
-- A --
| add_class_hierarchy_cache | Add a Class Hierarchy to the Cache |
| as.Multiplicity | Convert an object to a Multiplicity |
| assert_graph | Assertion for mlr3pipelines Graph |
| assert_pipeop | Assertion for mlr3pipelines PipeOp |
| as_graph | Conversion to mlr3pipelines Graph |
| as_pipeop | Conversion to mlr3pipelines PipeOp |
-- C --
| chain_graphs | Chain a Series of Graphs |
| concat_graphs | PipeOp Composition Operator |
-- F --
| filter_noop | Remove NO_OPs from a List |
-- G --
| Graph | Graph Base Class |
| GraphLearner | Encapsulate a Graph as a Learner |
| greplicate | Create Disjoint Graph Union of Copies of a Graph |
| gunion | Disjoint Union of Graphs |
-- I --
| is.Multiplicity | Check if an object is a Multiplicity |
| is_noop | Test for NO_OP |
-- L --
| LearnerClassifAvg | Optimized Weighted Average of Features for Classification and Regression |
| LearnerRegrAvg | Optimized Weighted Average of Features for Classification and Regression |
-- M --
| mlr3pipelines | mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3' |
| 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_classif.avg | Optimized Weighted Average of Features for Classification and Regression |
| mlr_learners_graph | Encapsulate a Graph as a Learner |
| mlr_learners_regr.avg | Optimized Weighted Average of Features for Classification and Regression |
| mlr_pipeops | Dictionary of PipeOps |
| 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 |
| Multiplicity | Multiplicity |
-- N --
| NO_OP | No-Op Sentinel Used for Alternative Branching |
-- P --
| 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 Re-Transform the Target Variable |
| PipeOp | PipeOp Base Class |
| PipeOpBoxCox | Box-Cox Transformation of Numeric Features |
| PipeOpBranch | Path Branching |
| PipeOpChunk | Chunk Input into Multiple Outputs |
| PipeOpClassBalancing | Class Balancing |
| PipeOpClassifAvg | Majority Vote Prediction |
| PipeOpClassWeights | Class Weights for Sample Weighting |
| PipeOpColApply | Apply a Function to each Column of a Task |
| PipeOpCollapseFactors | Collapse Factors |
| PipeOpColRoles | Change Column Roles of a Task |
| PipeOpCopy | Copy Input Multiple Times |
| PipeOpDateFeatures | Preprocess Date Features |
| PipeOpEncode | Factor Encoding |
| PipeOpEncodeImpact | Conditional Target Value Impact Encoding |
| PipeOpEncodeLmer | Impact Encoding with Random Intercept Models |
| PipeOpEnsemble | Ensembling Base Class |
| PipeOpFeatureUnion | Aggregate Features from Multiple Inputs |
| PipeOpFilter | Feature Filtering |
| PipeOpFixFactors | Fix Factor Levels |
| PipeOpHistBin | Split Numeric Features into Equally Spaced Bins |
| PipeOpICA | Independent Component Analysis |
| PipeOpImpute | Imputation Base Class |
| PipeOpImputeConstant | Impute Features by a Constant |
| PipeOpImputeHist | Impute Numerical Features by Histogram |
| PipeOpImputeLearner | Impute Features by Fitting a Learner |
| PipeOpImputeMean | Impute Numerical Features by their Mean |
| PipeOpImputeMedian | Impute Numerical Features by their Median |
| PipeOpImputeMode | Impute Features by their Mode |
| PipeOpImputeOOR | Out of Range Imputation |
| PipeOpImputeSample | Impute Features by Sampling |
| PipeOpKernelPCA | Kernelized Principle Component Analysis |
| PipeOpLearner | Wrap a Learner into a PipeOp |
| PipeOpLearnerCV | Wrap a Learner into a PipeOp with Cross-validated Predictions as Features |
| PipeOpMissInd | Add Missing Indicator Columns |
| PipeOpModelMatrix | Transform Columns by Constructing a Model Matrix |
| PipeOpMultiplicityExply | Explicate a Multiplicity |
| PipeOpMultiplicityImply | Implicate a Multiplicity |
| PipeOpMutate | Add Features According to Expressions |
| PipeOpNMF | Non-negative Matrix Factorization |
| PipeOpNOP | Simply Push Input Forward |
| PipeOpOVRSplit | Split a Classification Task into Binary Classification Tasks |
| PipeOpOVRUnite | Unite Binary Classification Tasks |
| PipeOpPCA | Principle Component Analysis |
| PipeOpProxy | Wrap another PipeOp or Graph as a Hyperparameter |
| PipeOpQuantileBin | Split Numeric Features into Quantile Bins |
| PipeOpRandomProjection | Project Numeric Features onto a Randomly Sampled Subspace |
| PipeOpRandomResponse | Generate a Randomized Response Prediction |
| PipeOpRegrAvg | Weighted Prediction Averaging |
| PipeOpRemoveConstants | Remove Constant Features |
| PipeOpRenameColumns | Rename Columns |
| PipeOpReplicate | Replicate the Input as a Multiplicity |
| PipeOpScale | Center and Scale Numeric Features |
| PipeOpScaleMaxAbs | Scale Numeric Features with Respect to their Maximum Absolute Value |
| PipeOpScaleRange | Linearly Transform Numeric Features to Match Given Boundaries |
| PipeOpSelect | Remove Features Depending on a Selector |
| PipeOpSmote | SMOTE Balancing |
| PipeOpSpatialSign | Normalize Data Row-wise |
| PipeOpSubsample | Subsampling |
| PipeOpTargetInvert | Invert Target Transformations |
| PipeOpTargetMutate | Transform a Target by a Function |
| PipeOpTargetTrafo | Target Transformation Base Class |
| PipeOpTargetTrafoScaleRange | Linearly Transform a Numeric Target to Match Given Boundaries |
| PipeOpTaskPreproc | Task Preprocessing Base Class |
| PipeOpTaskPreprocSimple | Simple Task Preprocessing Base Class |
| PipeOpTextVectorizer | Bag-of-word Representation of Character Features |
| PipeOpThreshold | Change the Threshold of a Classification Prediction |
| PipeOpTuneThreshold | Tune the Threshold of a Classification Prediction |
| PipeOpUnbranch | Unbranch Different Paths |
| PipeOpUpdateTarget | Transform a Target without an Explicit Inversion |
| PipeOpVtreat | Interface to the vtreat Package |
| PipeOpYeoJohnson | Yeo-Johnson Transformation of Numeric Features |
| po | Shorthand PipeOp Constructor |
| pos | Shorthand PipeOp Constructor |
| ppl | Shorthand Graph Constructor |
| ppls | Shorthand Graph Constructor |
-- R --
| register_autoconvert_function | Add Autoconvert Function to Conversion Register |
| reset_autoconvert_register | Reset Autoconvert Register |
| reset_class_hierarchy_cache | Reset the Class Hierarchy Cache |
-- S --
| Selector | Selector Functions |
| selector_all | Selector Functions |
| selector_cardinality_greater_than | Selector Functions |
| selector_grep | Selector Functions |
| selector_intersect | Selector Functions |
| selector_invert | Selector Functions |
| selector_missing | Selector Functions |
| selector_name | Selector Functions |
| selector_none | Selector Functions |
| selector_setdiff | Selector Functions |
| selector_type | Selector Functions |
| selector_union | Selector Functions |
| set_validate.GraphLearner | Configure Validation for a GraphLearner |
-- misc --
| %>>!% | PipeOp Composition Operator |
| %>>% | PipeOp Composition Operator |