Man pages for mlr3pipelines
Preprocessing Operators and Pipelines for 'mlr3'

add_class_hierarchy_cacheAdd a Class Hierarchy to the Cache
as_graphConversion to mlr3pipelines Graph
as.MultiplicityConvert an object to a Multiplicity
as_pipeopConversion to mlr3pipelines PipeOp
assert_graphAssertion for mlr3pipelines Graph
assert_pipeopAssertion for mlr3pipelines PipeOp
chain_graphsChain a Series of Graphs
filter_noopRemove NO_OPs from a List
grapes-greater-than-greater-than-grapesPipeOp Composition Operator
GraphGraph Base Class
greplicateCreate Disjoint Graph Union of Copies of a Graph
gunionDisjoint Union of Graphs
is.MultiplicityCheck if an object is a Multiplicity
is_noopTest for NO_OP
mlr3pipelines-packagemlr3pipelines: Preprocessing Operators and Pipelines for...
mlr_graphsDictionary of (sub-)graphs
mlr_graphs_baggingCreate a bagging learner
mlr_graphs_branchBranch Between Alternative Paths
mlr_graphs_greplicateCreate Disjoint Graph Union of Copies of a Graph
mlr_graphs_ovrCreate A Graph to Perform "One vs. Rest" classification.
mlr_graphs_robustifyRobustify a learner
mlr_graphs_stackingCreate A Graph to Perform Stacking.
mlr_graphs_targettrafoTransform and Re-Transform the Target Variable
mlr_learners_avgOptimized Weighted Average of Features for Classification and...
mlr_learners_graphEncapsulate a Graph as a Learner
mlr_pipeopsDictionary of PipeOps
mlr_pipeops_boxcoxBox-Cox Transformation of Numeric Features
mlr_pipeops_branchPath Branching
mlr_pipeops_chunkChunk Input into Multiple Outputs
mlr_pipeops_classbalancingClass Balancing
mlr_pipeops_classifavgMajority Vote Prediction
mlr_pipeops_classweightsClass Weights for Sample Weighting
mlr_pipeops_colapplyApply a Function to each Column of a Task
mlr_pipeops_collapsefactorsCollapse Factors
mlr_pipeops_colrolesChange Column Roles of a Task
mlr_pipeops_copyCopy Input Multiple Times
mlr_pipeops_datefeaturesPreprocess Date Features
mlr_pipeops_encodeFactor Encoding
mlr_pipeops_encodeimpactConditional Target Value Impact Encoding
mlr_pipeops_encodelmerImpact Encoding with Random Intercept Models
mlr_pipeops_featureunionAggregate Features from Multiple Inputs
mlr_pipeops_filterFeature Filtering
mlr_pipeops_fixfactorsFix Factor Levels
mlr_pipeops_histbinSplit Numeric Features into Equally Spaced Bins
mlr_pipeops_icaIndependent Component Analysis
mlr_pipeops_imputeconstantImpute Features by a Constant
mlr_pipeops_imputehistImpute Numerical Features by Histogram
mlr_pipeops_imputelearnerImpute Features by Fitting a Learner
mlr_pipeops_imputemeanImpute Numerical Features by their Mean
mlr_pipeops_imputemedianImpute Numerical Features by their Median
mlr_pipeops_imputemodeImpute Features by their Mode
mlr_pipeops_imputeoorOut of Range Imputation
mlr_pipeops_imputesampleImpute Features by Sampling
mlr_pipeops_kernelpcaKernelized Principle Component Analysis
mlr_pipeops_learnerWrap a Learner into a PipeOp
mlr_pipeops_learner_cvWrap a Learner into a PipeOp with Cross-validated Predictions...
mlr_pipeops_missindAdd Missing Indicator Columns
mlr_pipeops_modelmatrixTransform Columns by Constructing a Model Matrix
mlr_pipeops_multiplicityexplyExplicate a Multiplicity
mlr_pipeops_multiplicityimplyImplicate a Multiplicity
mlr_pipeops_mutateAdd Features According to Expressions
mlr_pipeops_nmfNon-negative Matrix Factorization
mlr_pipeops_nopSimply Push Input Forward
mlr_pipeops_ovrsplitSplit a Classification Task into Binary Classification Tasks
mlr_pipeops_ovruniteUnite Binary Classification Tasks
mlr_pipeops_pcaPrinciple Component Analysis
mlr_pipeops_proxyWrap another PipeOp or Graph as a Hyperparameter
mlr_pipeops_quantilebinSplit Numeric Features into Quantile Bins
mlr_pipeops_randomprojectionProject Numeric Features onto a Randomly Sampled Subspace
mlr_pipeops_randomresponseGenerate a Randomized Response Prediction
mlr_pipeops_regravgWeighted Prediction Averaging
mlr_pipeops_removeconstantsRemove Constant Features
mlr_pipeops_renamecolumnsRename Columns
mlr_pipeops_replicateReplicate the Input as a Multiplicity
mlr_pipeops_scaleCenter and Scale Numeric Features
mlr_pipeops_scalemaxabsScale Numeric Features with Respect to their Maximum Absolute...
mlr_pipeops_scalerangeLinearly Transform Numeric Features to Match Given Boundaries
mlr_pipeops_selectRemove Features Depending on a Selector
mlr_pipeops_smoteSMOTE Balancing
mlr_pipeops_spatialsignNormalize Data Row-wise
mlr_pipeops_subsampleSubsampling
mlr_pipeops_targetinvertInvert Target Transformations
mlr_pipeops_targetmutateTransform a Target by a Function
mlr_pipeops_targettrafoscalerangeLinearly Transform a Numeric Target to Match Given Boundaries
mlr_pipeops_textvectorizerBag-of-word Representation of Character Features
mlr_pipeops_thresholdChange the Threshold of a Classification Prediction
mlr_pipeops_tunethresholdTune the Threshold of a Classification Prediction
mlr_pipeops_unbranchUnbranch Different Paths
mlr_pipeops_updatetargetTransform a Target without an Explicit Inversion
mlr_pipeops_vtreatInterface to the vtreat Package
mlr_pipeops_yeojohnsonYeo-Johnson Transformation of Numeric Features
MultiplicityMultiplicity
NO_OPNo-Op Sentinel Used for Alternative Branching
PipeOpPipeOp Base Class
PipeOpEnsembleEnsembling Base Class
PipeOpImputeImputation Base Class
PipeOpTargetTrafoTarget Transformation Base Class
PipeOpTaskPreprocTask Preprocessing Base Class
PipeOpTaskPreprocSimpleSimple Task Preprocessing Base Class
poShorthand PipeOp Constructor
pplShorthand Graph Constructor
reexportsObjects exported from other packages
register_autoconvert_functionAdd Autoconvert Function to Conversion Register
reset_autoconvert_registerReset Autoconvert Register
reset_class_hierarchy_cacheReset the Class Hierarchy Cache
SelectorSelector Functions
mlr3pipelines documentation built on Sept. 21, 2022, 9:09 a.m.