reset

Reset pipeline or component

Since R2026a

Syntax

unlearnedPipe = reset(learnedPipe)

unlearnedPipe = reset(learnedPipe,componentName)

unlearnedPipe = reset(___,ResetParameters=true)

Description

unlearnedPipe = reset(learnedPipe) resets all data-dependent parameters (learnables) of the pipeline or component learnedPipe. The function unlocks learn parameters, but keeps any learn and run parameters previously set. The returned pipeline or component unlearnedPipe is ready to learn with new data.

unlearnedPipe = reset(learnedPipe,componentName) resets all learnables of the componentName component and any downstream components in the pipeline learnedPipe.

unlearnedPipe = reset(___,ResetParameters=true) also resets all learn and run parameters, using any of the input argument combinations in previous syntaxes.

You cannot reset structural parameters. Instead, you must create a component with new structural parameters.

Examples

Reset Pipeline After Learning

Create a simple pipeline with two components in series.

pca = pcaComponent(VarianceExplained=0.95);
ecoc = classificationECOCComponent;
pipeline = series(pca,ecoc)

pipeline = 

  LearningPipeline with properties:

             Name: "defaultName"
           Inputs: ["DataIn"    "Response"]
        InputTags: [1 2]
          Outputs: ["Predictions"    "Scores"    "Loss"]
       OutputTags: [1 0 0]

       Components: struct with 2 entries
      Connections: [6×2 table]

    HasLearnables: true
       HasLearned: false


Show summary of the components

The pipeline has two components with learnable parameters.

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. To see the learned parameters, index into the components.

learnedPipeline = learn(pipeline,X,Y)
learnedPCA = learnedPipeline.Components.PCA
learnedECOC = learnedPipeline.Components.ClassificationECOC

learnedPipeline = 

  LearningPipeline with properties:

             Name: "defaultName"
           Inputs: ["DataIn"    "Response"]
        InputTags: [1 2]
          Outputs: ["Predictions"    "Scores"    "Loss"]
       OutputTags: [1 0 0]

       Components: struct with 2 entries
      Connections: [6×2 table]

    HasLearnables: true
       HasLearned: true


Show summary of the components


learnedPCA = 

  pcaComponent with properties:

                 Name: "PCA"
               Inputs: "DataIn"
            InputTags: 1
              Outputs: "DataOut"
           OutputTags: 1

   
Learnables (HasLearned = true)
                   Mu: [5.8433 3.0573 3.7580 1.1993]
         Coefficients: [4×2 double]
        UsedVariables: ["SepalLength"    "SepalWidth"    "PetalLength"    "PetalWidth"]

   
Structural Parameters (locked)
           UseWeights: 0

   
Learn Parameters (locked)
    VarianceExplained: 0.9500


Show all parameters


learnedECOC = 

  classificationECOCComponent with properties:

            Name: "ClassificationECOC"
          Inputs: ["Predictors"    "Response"]
       InputTags: [1 2]
         Outputs: ["Predictions"    "Scores"    "Loss"]
      OutputTags: [1 0 0]

   
Learnables (HasLearned = true)
    TrainedModel: [1×1 classreg.learning.classif.CompactClassificationECOC]

   
Structural Parameters (locked)
      UseWeights: 0


Show all parameters

The components show the learned values, and the HasLearned property of the pipeline is set to true.

Reset the pipeline and investigate the components.

resetPipeline = reset(learnedPipeline)
resetPCA = resetPipeline.Components.PCA
resetECOC = resetPipeline.Components.ClassificationECOC

resetPipeline = 

  LearningPipeline with properties:

             Name: "defaultName"
           Inputs: ["DataIn"    "Response"]
        InputTags: [1 2]
          Outputs: ["Predictions"    "Scores"    "Loss"]
       OutputTags: [1 0 0]

       Components: struct with 2 entries
      Connections: [6×2 table]

    HasLearnables: true
       HasLearned: false


Show summary of the components


resetPCA = 

  pcaComponent with properties:

                 Name: "PCA"
               Inputs: "DataIn"
            InputTags: 1
              Outputs: "DataOut"
           OutputTags: 1

   
Learnables (HasLearned = false)
                   Mu: []
         Coefficients: []
        UsedVariables: []

   
Structural Parameters (locked)
           UseWeights: 0

   
Learn Parameters (unlocked)
    VarianceExplained: 0.9500


Show all parameters


resetECOC = 

  classificationECOCComponent with properties:

            Name: "ClassificationECOC"
          Inputs: ["Predictors"    "Response"]
       InputTags: [1 2]
         Outputs: ["Predictions"    "Scores"    "Loss"]
      OutputTags: [1 0 0]

   
Learnables (HasLearned = false)
    TrainedModel: []

   
Structural Parameters (locked)
      UseWeights: 0


Show all parameters

The software resets all the learnable parameters of the components, and sets the HasLearned property to false.

Input Arguments

learnedPipe — Pipeline or component to reset

LearningPipeline object | learning component object

Pipeline or component to reset, 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

componentName — Name of component to reset

character vector | string scalar

Name of the component to reset, specified as a character vector or string scalar. You can find the name of a component in a pipeline by indexing into the Components property using learnedPipe.Components.

Data Types: char | string

Output Arguments

unlearnedPipe — Pipeline or component with reset data-dependent parameters

LearningPipeline object | learning component object

Pipeline or component with reset data-dependent parameters, returned as a LearningPipeline or learning component object. If unlearnedPipe is a pipeline, the software either resets all its components or resets only the component specified by componentName and its downstream components.

Version History

Introduced in R2026a