parallel

Connect components or pipelines in parallel to create pipeline

Since R2026a

Syntax

pipeline = parallel(pipe1,...,pipeN)

pipeline = parallel(pipe1,...,pipeN,ExpandSubpipelines=expandflag)

Description

pipeline = parallel(pipe1,...,pipeN) creates a pipeline (pipeline) by connecting components or pipelines pipe1 through pipeN in parallel. Use parallel to model conceptually simultaneous, order-independent processes or workflows.

parallel creates a pipeline with input and output ports determined by the unique tags in the inputs and outputs of pipe1,...,pipeN. Each unique tag creates a unique port. The function then connects the inputs and outputs of pipe1,...,pipeN to the pipeline inputs and outputs with matching tags. During pipeline execution, data from each pipeline input is copied to each connected component input. Data from component outputs are joined together at the pipeline outputs to which they connect. The join operation defaults to horizontal concatenation.

pipe1 through pipeN must have unique names. parallel renames any component that has the same name as another component already in the pipeline. New names consist of the original name with a numeric suffix.

pipeline = parallel(pipe1,...,pipeN,ExpandSubpipelines=expandflag) creates a pipeline where the pipeline elements of pipe1,...,pipeN are either expanded or collapsed, based on the value of expandflag.

Examples

Create Pipeline with Parallel Components

Create a pipeline by connecting two components in parallel.

normalizer = normalizerComponent;
oneHotEncoder = oneHotEncoderComponent;
pipeline = parallel(normalizer,oneHotEncoder)

pipeline = 

  LearningPipeline with properties:

             Name: "defaultName"
           Inputs: "DataIn"
        InputTags: 1
          Outputs: "DataCombined"
       OutputTags: 1

       Components: struct with 2 entries
      Connections: [4×2 table]

    HasLearnables: true
       HasLearned: false


Show summary of the components

Input Arguments

pipe — Element to connect in parallel

LearningPipeline object | learning component object

Element to connect to another element in parallel, specified as a LearningPipeline object or a learning component object in one of the following tables.

Data Preprocessing Components

Component	Purpose
equalWidthBinnerComponent	Grouping data into equal-width bins
frequencyEncoderComponent	Frequency encoding categorical variables
kmeansEncoderComponent	Feature extraction using k-means clustering
normalizerComponent	Normalizing data
observationImputerComponent	Imputing missing values
observationRemoverComponent	Removing observations
oneHotEncoderComponent	Encoding categorical data into one-hot vectors
outlierImputerComponent	Imputing outlier values
outlierRemoverComponent	Removing outlier values
pcaComponent	Principal component analysis (PCA)
quantileBinnerComponent	Binning data into equally probable bins
ricaComponent	Feature extraction using reconstruction independent component analysis (RICA)
sparseFilterComponent	Feature extraction using sparse filtering

Feature Selection and Engineering Components

Component	Purpose
featureSelectionClassificationANOVAComponent	Feature selection using one-way ANOVA test
featureSelectionClassificationChi2Component	Feature selection using chi-square tests
featureSelectionClassificationKruskalWallisComponent	Feature selection using Kruskal-Wallis test
featureSelectionClassificationMRMRComponent	Minimum redundancy maximum relevance (MRMR) feature selection in classification workflow
featureSelectionClassificationNCAComponent	Neighborhood component analysis (NCA) feature selection in classification workflow
featureSelectionClassificationReliefFComponent	ReliefF feature selection in classification workflow
featureSelectionRegressionFTestComponent	Feature selection using F-tests
featureSelectionRegressionMRMRComponent	Minimum redundancy maximum relevance (MRMR) feature selection in regression workflow
featureSelectionRegressionNCAComponent	Neighborhood component analysis (NCA) feature selection in regression workflow
featureSelectionRegressionReliefFComponent	ReliefF feature selection in regression workflow
variableSelectorComponent	Manual variable selection

Classification Model Components

Component	Purpose
classificationDiscriminantComponent	Discriminant analysis classification
classificationECOCComponent	Multiclass classification using error-correcting output codes (ECOC) model
classificationEnsembleComponent	Ensemble classification
classificationGAMComponent	Binary classification using generalized additive model (GAM)
classificationKernelComponent	Classification using Gaussian kernel with random feature expansion
classificationKNNComponent	Classification using k-nearest neighbor model
classificationLinearComponent	Binary classification of high-dimensional data using a linear model
classificationNaiveBayesComponent	Multiclass classification using a naive Bayes model
classificationNeuralNetworkComponent	Classification using a neural network model
classificationSVMComponent	One-class and binary classification using a support vector machine (SVM) classifier
classificationTreeComponent	Decision tree classifier

Regression Model Components

Component	Purpose
regressionEnsembleComponent	Ensemble regression
regressionGAMComponent	Regression using generalized additive model (GAM)
regressionGPComponent	Gaussian process regression
regressionKernelComponent	Kernel regression using explicit feature expansion
regressionLinearComponent	Linear regression
regressionNeuralNetworkComponent	Neural network regression
regressionSVMComponent	Regression using a support vector machine (SVM)
regressionTreeComponent	Decision tree regression

Custom Components

Component	Purpose
functionComponent	Custom function

expandflag — Flag for expanding subpipelines

true or 1 (default) | false or 0

Flag for expanding the subpipelines of pipeline, specified as true (1) or false (0). A value of true expands all the pipeline elements of pipe1,...,pipeN in pipeline. A value of false collapses all the pipeline elements of pipe1,...,pipeN.

You can choose to expand subpipelines after creating a pipeline by using the expand function.

Data Types: logical

Output Arguments

pipeline — New pipeline created from components or pipelines

LearningPipeline object

New pipeline created from components or pipelines, returned as a LearningPipeline object.

Version History

Introduced in R2026a