series

Connect components in series to create pipeline

Since R2026a

Syntax

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

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

Description

pipeline = series(pipe1,...,pipeN) creates a pipeline (pipeline) by connecting the components or pipelines pipe1 through pipeN in an ordered series. Use series to model sequential, order-dependent processes or workflows.

series connects the outputs of pipe1 to the inputs of pipe2 by matching port tags. If no matches are found, no connections are made. pipe1 and pipe2 then form a pipeline and the process repeats. That is, the function sequentially appends the remaining pipe3 through pipeN components or pipelines to the pipeline. This process might leave some ports unconnected. These unconnected ports become inputs and outputs of the pipeline.

pipe1 through pipeN must have unique names. series 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 = series(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 in Series

Create a pipeline by connecting two components in series.

removeMissing = observationRemoverComponent;
normalizer = normalizerComponent;
pipeline = series(removeMissing,normalizer)

pipeline = 

  LearningPipeline with properties:

             Name: "defaultName"
           Inputs: ["DataIn1"    "DataIn2"]
        InputTags: [1 2]
          Outputs: ["DataScaled"    "DataOut2"]
       OutputTags: [1 2]

       Components: struct with 2 entries
      Connections: [5×2 table]

    HasLearnables: true
       HasLearned: false


Show summary of the components

Input Arguments

pipe — Element to connect in series

LearningPipeline object | learning component object

Element to connect to another element in series, 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