
Function reference
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mlr3pipelines-package
- mlr3pipelines: Preprocessing Operators and Pipelines for 'mlr3'
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PipeOp
- PipeOp Base Class
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Graph
- Graph Base Class
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PipeOpTaskPreproc
- Task Preprocessing Base Class
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PipeOpTaskPreprocSimple
- Simple Task Preprocessing Base Class
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PipeOpImpute
- Imputation Base Class
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Multiplicity()
- Multiplicity
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`%>>%`
concat_graphs()
`%>>!%`
- PipeOp Composition Operator
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gunion()
- Disjoint Union of Graphs
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greplicate()
- Create Disjoint Graph Union of Copies of a Graph
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mlr_pipeops
- Dictionary of PipeOps
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mlr_pipeops_boxcox
- Box-Cox Transformation of Numeric Features
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mlr_pipeops_branch
- Path Branching
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mlr_pipeops_chunk
- Chunk Input into Multiple Outputs
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mlr_pipeops_classbalancing
- Class Balancing
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mlr_pipeops_classifavg
- Majority Vote Prediction
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mlr_pipeops_classweights
- Class Weights for Sample Weighting
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mlr_pipeops_colapply
- Apply a Function to each Column of a Task
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mlr_pipeops_collapsefactors
- Collapse Factors
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mlr_pipeops_colroles
- Change Column Roles of a Task
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mlr_pipeops_copy
- Copy Input Multiple Times
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mlr_pipeops_datefeatures
- Preprocess Date Features
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mlr_pipeops_encode
- Factor Encoding
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mlr_pipeops_encodeimpact
- Conditional Target Value Impact Encoding
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mlr_pipeops_encodelmer
- Impact Encoding with Random Intercept Models
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mlr_pipeops_featureunion
- Aggregate Features from Multiple Inputs
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mlr_pipeops_filter
- Feature Filtering
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mlr_pipeops_fixfactors
- Fix Factor Levels
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mlr_pipeops_histbin
- Split Numeric Features into Equally Spaced Bins
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mlr_pipeops_ica
- Independent Component Analysis
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mlr_pipeops_imputeconstant
- Impute Features by a Constant
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mlr_pipeops_imputehist
- Impute Numerical Features by Histogram
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mlr_pipeops_imputelearner
- Impute Features by Fitting a Learner
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mlr_pipeops_imputemean
- Impute Numerical Features by their Mean
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mlr_pipeops_imputemedian
- Impute Numerical Features by their Median
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mlr_pipeops_imputemode
- Impute Features by their Mode
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mlr_pipeops_imputeoor
- Out of Range Imputation
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mlr_pipeops_imputesample
- Impute Features by Sampling
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mlr_pipeops_kernelpca
- Kernelized Principle Component Analysis
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mlr_pipeops_learner
- Wrap a Learner into a PipeOp
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mlr_pipeops_learner_cv
- Wrap a Learner into a PipeOp with Cross-validated Predictions as Features
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mlr_pipeops_missind
- Add Missing Indicator Columns
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mlr_pipeops_modelmatrix
- Transform Columns by Constructing a Model Matrix
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mlr_pipeops_multiplicityexply
- Explicate a Multiplicity
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mlr_pipeops_multiplicityimply
- Implicate a Multiplicity
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mlr_pipeops_mutate
- Add Features According to Expressions
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mlr_pipeops_nmf
- Non-negative Matrix Factorization
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mlr_pipeops_nop
- Simply Push Input Forward
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mlr_pipeops_ovrsplit
- Split a Classification Task into Binary Classification Tasks
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mlr_pipeops_ovrunite
- Unite Binary Classification Tasks
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mlr_pipeops_pca
- Principle Component Analysis
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mlr_pipeops_proxy
- Wrap another PipeOp or Graph as a Hyperparameter
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mlr_pipeops_quantilebin
- Split Numeric Features into Quantile Bins
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mlr_pipeops_randomprojection
- Project Numeric Features onto a Randomly Sampled Subspace
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mlr_pipeops_randomresponse
- Generate a Randomized Response Prediction
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mlr_pipeops_regravg
- Weighted Prediction Averaging
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mlr_pipeops_removeconstants
- Remove Constant Features
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mlr_pipeops_renamecolumns
- Rename Columns
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mlr_pipeops_replicate
- Replicate the Input as a Multiplicity
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mlr_pipeops_scale
- Center and Scale Numeric Features
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mlr_pipeops_scalemaxabs
- Scale Numeric Features with Respect to their Maximum Absolute Value
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mlr_pipeops_scalerange
- Linearly Transform Numeric Features to Match Given Boundaries
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mlr_pipeops_select
- Remove Features Depending on a Selector
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mlr_pipeops_smote
- SMOTE Balancing
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mlr_pipeops_spatialsign
- Normalize Data Row-wise
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mlr_pipeops_subsample
- Subsampling
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mlr_pipeops_targetinvert
- Invert Target Transformations
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mlr_pipeops_targetmutate
- Transform a Target by a Function
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mlr_pipeops_targettrafoscalerange
- Linearly Transform a Numeric Target to Match Given Boundaries
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mlr_pipeops_textvectorizer
- Bag-of-word Representation of Character Features
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mlr_pipeops_threshold
- Change the Threshold of a Classification Prediction
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mlr_pipeops_tunethreshold
- Tune the Threshold of a Classification Prediction
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mlr_pipeops_unbranch
- Unbranch Different Paths
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mlr_pipeops_updatetarget
- Transform a Target without an Explicit Inversion
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mlr_pipeops_vtreat
- Interface to the vtreat Package
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mlr_pipeops_yeojohnson
- Yeo-Johnson Transformation of Numeric Features
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PipeOp
- PipeOp Base Class
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PipeOpEnsemble
- Ensembling Base Class
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PipeOpImpute
- Imputation Base Class
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PipeOpTargetTrafo
- Target Transformation Base Class
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PipeOpTaskPreproc
- Task Preprocessing Base Class
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PipeOpTaskPreprocSimple
- Simple Task Preprocessing Base Class
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pipeline_bagging()
- Create a bagging learner
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pipeline_branch()
- Branch Between Alternative Paths
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pipeline_greplicate()
- Create Disjoint Graph Union of Copies of a Graph
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pipeline_ovr()
- Create A Graph to Perform "One vs. Rest" classification.
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pipeline_robustify()
- Robustify a learner
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pipeline_stacking()
- Create A Graph to Perform Stacking.
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pipeline_targettrafo()
- Transform and Re-Transform the Target Variable
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mlr_graphs
- Dictionary of (sub-)graphs
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pipeline_bagging()
- Create a bagging learner
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pipeline_branch()
- Branch Between Alternative Paths
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pipeline_greplicate()
- Create Disjoint Graph Union of Copies of a Graph
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pipeline_ovr()
- Create A Graph to Perform "One vs. Rest" classification.
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pipeline_robustify()
- Robustify a learner
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pipeline_stacking()
- Create A Graph to Perform Stacking.
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pipeline_targettrafo()
- Transform and Re-Transform the Target Variable
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chain_graphs()
- Chain a Series of Graphs
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mlr_learners_graph
- Encapsulate a Graph as a Learner
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mlr_learners_classif.avg
mlr_learners_regr.avg
- Optimized Weighted Average of Features for Classification and Regression
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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
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as_graph()
- Conversion to mlr3pipelines Graph
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assert_graph()
- Assertion for mlr3pipelines Graph
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as_pipeop()
- Conversion to mlr3pipelines PipeOp
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assert_pipeop()
- Assertion for mlr3pipelines PipeOp
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is_noop()
- Test for NO_OP
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NO_OP
- No-Op Sentinel Used for Alternative Branching
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filter_noop()
- Remove NO_OPs from a List
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as.Multiplicity()
- Convert an object to a Multiplicity
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is.Multiplicity()
- Check if an object is a Multiplicity
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PipeOpEnsemble
- Ensembling Base Class
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add_class_hierarchy_cache()
- Add a Class Hierarchy to the Cache
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reset_class_hierarchy_cache()
- Reset the Class Hierarchy Cache
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register_autoconvert_function()
- Add Autoconvert Function to Conversion Register
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reset_autoconvert_register()
- Reset Autoconvert Register