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 theionospheredata set. Remove the first two predictors for stability.

loadionosphereX = X(:,3:end);

Train a naive Bayes classifier using the predictorsXand class labelsY。推荐的做法是指定类names.fitcnbassumes 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} Properties, Methods

Mdlis a trainedClassificationNaiveBayesclassifier.

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} Properties, Methods

CMdlis a trainedCompactClassificationNaiveBayesclassifier.

Display the amount of memory used by each classifier.

whos('Mdl','CMdl')

Name Size Bytes Class Attributes CMdl 1x1 15060 classreg.learning.classif.CompactClassificationNaiveBayes Mdl 1x1 111190 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 removeMdlfrom the MATLAB® Workspace, and then passCMdland new predictor values topredict。

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

Load theionospheredata set.

loadionosphere

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: -0.1343 KernelParameters: [1x1 struct] Mu: [0.8917 0 0.6413 0.0444 0.6011 0.1159 0.5501 ... ] Sigma: [0.3112 0 0.4977 0.4414 0.5199 0.4608 0.4927 ... ] BoxConstraints: [351x1 double] ConvergenceInfo: [1x1 struct] IsSupportVector: [351x1 logical] Solver: 'SMO' Properties, Methods

SVMModelis aClassificationSVMclassifier.

Reduce the size of the SVM classifier.

CompactSVMModel = compact(SVMModel)

CompactSVMModel = CompactClassificationSVM ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'b' 'g'} ScoreTransform: 'none' Alpha: [90x1 double] Bias: -0.1343 KernelParameters: [1x1 struct] Mu: [0.8917 0 0.6413 0.0444 0.6011 0.1159 0.5501 ... ] Sigma: [0.3112 0 0.4977 0.4414 0.5199 0.4608 0.4927 ... ] SupportVectors: [90x34 double] SupportVectorLabels: [90x1 double] Properties, Methods

CompactSVMModelis aCompactClassificationSVMclassifier.

Display the amount of memory used by each classifier.

whos('SVMModel','CompactSVMModel')

Name Size Bytes Class Attributes CompactSVMModel 1x1 31058 classreg.learning.classif.CompactClassificationSVM SVMModel 1x1 141148 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 removeSVMModelfrom the MATLAB® Workspace, and then passCompactSVMModeland new predictor values topredict。

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

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 thecarbigdata set.

loadcarbig

SpecifyAcceleration,Displacement,Horsepower, andWeightas the predictor variables (X) andMPGas the response variable (Y).

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

Train a GAM usingXandY。

Mdl = fitrgam(X,Y)

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

Mdlis aRegressionGAMmodel object.

Reduce the size of the model.

CMdl = compact(Mdl)

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

CMdlis aCompactRegressionGAMmodel object.

Display the amount of memory used by each regression model.

whos('Mdl','CMdl')

Name Size Bytes Class Attributes CMdl 1x1 578163 classreg.learning.regr.CompactRegressionGAM Mdl 1x1 611957 RegressionGAM

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

To efficiently predict responses for new observations, you can removeMdlfrom the MATLAB® Workspace, and then passCMdland new predictor values topredict。