Man pages for mlr3pipelines
Preprocessing Operators and Pipelines for 'mlr3'
| add_class_hierarchy_cache | Add a Class Hierarchy to the Cache |
| as_graph | Conversion to mlr3pipelines Graph |
| as.Multiplicity | Convert an object to a Multiplicity |
| 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 |
| CnfAtom | Atoms for CNF Formulas |
| CnfClause | Clauses in CNF Formulas |
| CnfFormula | CNF Formulas |
| CnfSymbol | Symbols for CNF Formulas |
| CnfUniverse | Symbol Table for CNF Formulas |
| filter_noop | Remove NO_OPs from a List |
| grapes-greater-than-greater-than-grapes | PipeOp Composition Operator |
| Graph | Graph Base Class |
| 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... |
| 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... |
| 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_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... |
| mlr_pipeops_learner_pi_cvplus | Wrap a Learner into a PipeOp with Cross-validation Plus... |
| mlr_pipeops_learner_quantiles | Wrap a Learner into a PipeOp to 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 | 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_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... |
| 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 |
| 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 |
| po | Shorthand PipeOp Constructor |
| ppl | Shorthand Graph Constructor |
| preproc | Simple Pre-processing |
| reexports | Objects exported from other packages |
| register_autoconvert_function | Add Autoconvert Function to Conversion Register |
| reset_autoconvert_register | Reset Autoconvert Register |
| reset_class_hierarchy_cache | Reset the Class Hierarchy Cache |
| Selector | Selector Functions |
| set_validate.GraphLearner | Configure Validation for a GraphLearner |
