view

View diagram of pipeline inputs, outputs, components, and connections

Since R2026a

Syntax

view(pipe)

Description

view(pipe) displays the pipeline or component pipe in a new window. Obround shapes indicate pipeline inputs and outputs, rounded rectangles indicate components, and arrows indicate component connections. Data passes through the pipeline from top to bottom. The software adds color to connections made automatically by matching the Tag properties of component ports.

Examples

View Pipeline Before and After Learning

Create a simple pipeline with two serial components. View the pipeline.

pca = pcaComponent(NumComponents=3);
ecoc = classificationECOCComponent;
pipeline = series(pca,ecoc);
view(pipeline)

The pipeline has two components with learnable parameters, neither of which has been learned yet. This pipeline has no components without learnable parameters. The display also shows the inputs and outputs of the pipeline and the internal data ports, such as DataOut from the PCA component.

Load sample data and define the predictor and response variables.

fisheriris = readtable("fisheriris.csv");
X = fisheriris(:,1:end-1);
Y = fisheriris(:,end);

Learn the pipeline by passing data through it. View the learned pipeline.

[learnedPipeline,predY,scores,loss] = learn(pipeline,X,Y);
view(learnedPipeline)

Note that the parameters in the two components have been learned. To see the learned parameters, index into the components, for example, learnedPipeline.Components.PCA.

For a more in-depth example that uses pipeline visualizations, see Create Simple Classification Pipeline.

Input Arguments

pipe — Pipeline or component to view

LearningPipeline object | learning component object

Pipeline or component to view, 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

Version History

Introduced in R2026a