compact

Reduce size of machine learning model

Syntax

CompactMdl = compact(Mdl)

Description

CompactMdl = compact(Mdl) returns a compact model (CompactMdl), the compact version of the trained machine learning model Mdl.

CompactMdl does not contain the training data, whereas Mdl contains the training data in its X and Y properties. Therefore, although you can predict class labels using CompactMdl, you cannot perform tasks such as cross-validation with the compact model.

Examples

Reduce Size of Naive Bayes Classifier

Reduce the size of a full naive Bayes classifier by removing the training data. Full naive Bayes classifiers hold the training data. You can use a compact naive Bayes classifier to improve memory efficiency.

Load the ionosphere data set. Remove the first two predictors for stability.

load ionosphere
X = X(:,3:end);

Train a naive Bayes classifier using the predictors X and class labels Y. A recommended practice is to specify the class names. fitcnb assumes that each predictor is conditionally and normally distributed.

Mdl = fitcnb(X,Y,'ClassNames',{'b','g'})

Mdl = 
  ClassificationNaiveBayes
              ResponseName: 'Y'
     CategoricalPredictors: []
                ClassNames: {'b'  'g'}
            ScoreTransform: 'none'
           NumObservations: 351
         DistributionNames: {1x32 cell}
    DistributionParameters: {2x32 cell}

Mdl is a trained ClassificationNaiveBayes classifier.

Reduce the size of the naive Bayes classifier.

CMdl = compact(Mdl)

CMdl = 
  CompactClassificationNaiveBayes
              ResponseName: 'Y'
     CategoricalPredictors: []
                ClassNames: {'b'  'g'}
            ScoreTransform: 'none'
         DistributionNames: {1x32 cell}
    DistributionParameters: {2x32 cell}

CMdl is a trained CompactClassificationNaiveBayes classifier.

Display the amount of memory used by each classifier.

whos('Mdl','CMdl')

  Name      Size             Bytes  Class                                                        Attributes

  CMdl      1x1              16436  classreg.learning.classif.CompactClassificationNaiveBayes              
  Mdl       1x1             112598  ClassificationNaiveBayes                                               

The full naive Bayes classifier (Mdl) is more than seven times larger than the compact naive Bayes classifier (CMdl).

To label new observations efficiently, you can remove Mdl from the MATLAB® Workspace, and then pass CMdl and new predictor values to predict.

Reduce Size of SVM Classifier

Reduce the size of a full support vector machine (SVM) classifier by removing the training data. Full SVM classifiers (that is, ClassificationSVM classifiers) hold the training data. To improve efficiency, use a smaller classifier.

Load the ionosphere data set.

load ionosphere

Train an SVM classifier. Standardize the predictor data and specify the order of the classes.

SVMModel = fitcsvm(X,Y,'Standardize',true,...
    'ClassNames',{'b','g'})

SVMModel = 
  ClassificationSVM
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
          NumObservations: 351
                    Alpha: [90x1 double]
                     Bias: -0.1342
         KernelParameters: [1x1 struct]
                       Mu: [0.8917 0 0.6413 0.0444 0.6011 0.1159 0.5501 0.1194 0.5118 0.1813 0.4762 0.1550 0.4008 0.0934 0.3442 0.0711 0.3819 -0.0036 0.3594 -0.0240 0.3367 0.0083 0.3625 -0.0574 0.3961 -0.0712 0.5416 -0.0695 0.3784 ... ] (1x34 double)
                    Sigma: [0.3112 0 0.4977 0.4414 0.5199 0.4608 0.4927 0.5207 0.5071 0.4839 0.5635 0.4948 0.6222 0.4949 0.6528 0.4584 0.6180 0.4968 0.6263 0.5191 0.6098 0.5182 0.6038 0.5275 0.5785 0.5085 0.5162 0.5500 0.5759 0.5080 ... ] (1x34 double)
           BoxConstraints: [351x1 double]
          ConvergenceInfo: [1x1 struct]
          IsSupportVector: [351x1 logical]
                   Solver: 'SMO'

SVMModel is a ClassificationSVM classifier.

Reduce the size of the SVM classifier.

CompactSVMModel = compact(SVMModel)

CompactSVMModel = 
  CompactClassificationSVM
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
                    Alpha: [90x1 double]
                     Bias: -0.1342
         KernelParameters: [1x1 struct]
                       Mu: [0.8917 0 0.6413 0.0444 0.6011 0.1159 0.5501 0.1194 0.5118 0.1813 0.4762 0.1550 0.4008 0.0934 0.3442 0.0711 0.3819 -0.0036 0.3594 -0.0240 0.3367 0.0083 0.3625 -0.0574 0.3961 -0.0712 0.5416 -0.0695 0.3784 ... ] (1x34 double)
                    Sigma: [0.3112 0 0.4977 0.4414 0.5199 0.4608 0.4927 0.5207 0.5071 0.4839 0.5635 0.4948 0.6222 0.4949 0.6528 0.4584 0.6180 0.4968 0.6263 0.5191 0.6098 0.5182 0.6038 0.5275 0.5785 0.5085 0.5162 0.5500 0.5759 0.5080 ... ] (1x34 double)
           SupportVectors: [90x34 double]
      SupportVectorLabels: [90x1 double]

CompactSVMModel is a CompactClassificationSVM classifier.

Display the amount of memory used by each classifier.

whos('SVMModel','CompactSVMModel')

  Name                 Size             Bytes  Class                                                 Attributes

  CompactSVMModel      1x1              30749  classreg.learning.classif.CompactClassificationSVM              
  SVMModel             1x1             140279  ClassificationSVM                                               

The full SVM classifier (SVMModel) is more than four times larger than the compact SVM classifier (CompactSVMModel).

To label new observations efficiently, you can remove SVMModel from the MATLAB® Workspace, and then pass CompactSVMModel and new predictor values to predict.

To further reduce the size of the compact SVM classifier, use the discardSupportVectors function to discard support vectors.

Reduce Size of Generalized Additive Model

Reduce the size of a full generalized additive model (GAM) for regression by removing the training data. Full models hold the training data. You can use a compact model to improve memory efficiency.

Load the carbig data set.

load carbig

Specify Acceleration, Displacement, Horsepower, and Weight as the predictor variables (X) and MPG as the response variable (Y).

X = [Acceleration,Displacement,Horsepower,Weight];
Y = MPG;

Train a GAM using X and Y.

Mdl = fitrgam(X,Y)

Mdl = 
  RegressionGAM
              ResponseName: 'Y'
     CategoricalPredictors: []
         ResponseTransform: 'none'
                 Intercept: 26.9442
    IsStandardDeviationFit: 0
           NumObservations: 398

Mdl is a RegressionGAM model object.

Reduce the size of the model.

CMdl = compact(Mdl)

CMdl = 
  CompactRegressionGAM
              ResponseName: 'Y'
     CategoricalPredictors: []
         ResponseTransform: 'none'
                 Intercept: 26.9442
    IsStandardDeviationFit: 0

CMdl is a CompactRegressionGAM model object.

Display the amount of memory used by each regression model.

whos('Mdl','CMdl')

  Name      Size             Bytes  Class                                          Attributes

  CMdl      1x1             597222  classreg.learning.regr.CompactRegressionGAM              
  Mdl       1x1             631046  RegressionGAM                                            

The full model (Mdl) is larger than the compact model (CMdl).

To efficiently predict responses for new observations, you can remove Mdl from the MATLAB® Workspace, and then pass CMdl and new predictor values to predict.

Input Arguments

Mdl — Machine learning model

full regression model object | full classification model object

Machine learning model, specified as a full regression or classification model object, as given in the following tables of supported models.

Regression Model Object

Model	Full Regression Model Object
Gaussian process regression (GPR) model	RegressionGP
Generalized additive model (GAM)	RegressionGAM
Neural network model	RegressionNeuralNetwork

Classification Model Object

Model	Full Classification Model Object
Generalized additive model	ClassificationGAM
Naive Bayes model	ClassificationNaiveBayes
Neural network model	ClassificationNeuralNetwork
Support vector machine for one-class and binary classification	ClassificationSVM

Output Arguments

CompactMdl — Compact machine learning model

compact regression model object | compact classification model object

Compact machine learning model, returned as one of the compact model objects in the following tables, depending on the input model Mdl.

Regression Model Object

Model	Full Model (Mdl)	Compact Model (CompactMdl)
Gaussian process regression (GPR) model	RegressionGP	CompactRegressionGP
Generalized additive model	RegressionGAM	CompactRegressionGAM
Neural network model	RegressionNeuralNetwork	CompactRegressionNeuralNetwork

Classification Model Object

Model	Full Model (Mdl)	Compact Model (CompactMdl)
Generalized additive model	ClassificationGAM	CompactClassificationGAM
Naive Bayes model	ClassificationNaiveBayes	CompactClassificationNaiveBayes
Neural network model	ClassificationNeuralNetwork	CompactClassificationNeuralNetwork
Support vector machine for one-class and binary classification	ClassificationSVM	CompactClassificationSVM

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• This function fully supports GPU arrays for a trained classification model specified as a ClassificationSVM or ClassificationNeuralNetwork object.

• This function fully supports GPU arrays for a trained regression model specified as a RegressionNeuralNetwork object.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2014a

R2024b: Specify GPU arrays for neural network models (requires Parallel Computing Toolbox)

compact fully supports GPU arrays for RegressionNeuralNetwork and ClassificationNeuralNetwork models.