resubMargin

Resubstitution classification margin

Syntax

m = resubMargin(Mdl)

m = resubMargin(Mdl,'IncludeInteractions',includeInteractions)

Description

m = resubMargin(Mdl) returns the resubstitution Classification Margin (m) for the trained classification model Mdl using the predictor data stored in Mdl.X and the corresponding true class labels stored in Mdl.Y.

m is returned as an n-by-1 numeric column vector, where n is the number of observations in the predictor data.

m = resubMargin(Mdl,'IncludeInteractions',includeInteractions) specifies whether to include interaction terms in computations. This syntax applies only to generalized additive models.

Examples

Estimate Resubstitution Classification Margins of Naive Bayes Classifier

Estimate the resubstitution (in-sample) classification margins of a naive Bayes classifier. An observation margin is the observed true class score minus the maximum false class score among all scores in the respective class.

Load the fisheriris data set. Create X as a numeric matrix that contains four measurements for 150 irises. Create Y as a cell array of character vectors that contains the corresponding iris species.

load fisheriris
X = meas;
Y = species;

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',{'setosa','versicolor','virginica'})

Mdl = 
  ClassificationNaiveBayes
              ResponseName: 'Y'
     CategoricalPredictors: []
                ClassNames: {'setosa'  'versicolor'  'virginica'}
            ScoreTransform: 'none'
           NumObservations: 150
         DistributionNames: {'normal'  'normal'  'normal'  'normal'}
    DistributionParameters: {3×4 cell}


  Properties, Methods

Mdl is a trained ClassificationNaiveBayes classifier.

Estimate the resubstitution classification margins.

m = resubMargin(Mdl);
median(m)

ans = 
1.0000

Display the histogram of the in-sample classification margins.

histogram(m,30,'Normalization','probability')
xlabel('In-Sample Margins')
ylabel('Probability')
title('Probability Distribution of the In-Sample Margins')

Classifiers that yield relatively large margins are preferred.

Select SVM Classifier Features by Examining In-Sample Margins

Perform feature selection by comparing in-sample margins from multiple models. Based solely on this comparison, the model with the highest margins is the best model.

Load the ionosphere data set. Define two data sets:

• fullX contains all predictors (except the removed column of 0s).

• partX contains the last 20 predictors.

load ionosphere
fullX = X;
partX = X(:,end-20:end);

Train a support vector machine (SVM) classifier for each predictor set.

FullSVMModel = fitcsvm(fullX,Y);
PartSVMModel = fitcsvm(partX,Y);

Estimate the in-sample margins for each classifier.

fullMargins = resubMargin(FullSVMModel);
partMargins = resubMargin(PartSVMModel);
n = size(X,1);
p = sum(fullMargins < partMargins)/n

p = 
0.2251

Approximately 22% of the margins from the full model are less than those from the model with fewer predictors. This result suggests that the model trained with all the predictors is better.

Compare GAMs by Examining Training Sample Margins and Edge

Compare a generalized additive model (GAM) with linear terms to a GAM with both linear and interaction terms by examining the training sample margins and edge. Based solely on this comparison, the classifier with the highest margins and edge is the best model.

Load the 1994 census data stored in census1994.mat. The data set consists of demographic data from the US Census Bureau to predict whether an individual makes over $50,000 per year. The classification task is to fit a model that predicts the salary category of people given their age, working class, education level, marital status, race, and so on.

load census1994

census1994 contains the training data set adultdata and the test data set adulttest. To reduce the running time for this example, subsample 500 training observations from adultdata by using the datasample function.

rng('default') % For reproducibility
NumSamples = 5e2;
adultdata = datasample(adultdata,NumSamples,'Replace',false);

Train a GAM that contains both linear and interaction terms for predictors. Specify to include all available interaction terms whose p-values are not greater than 0.05.

Mdl = fitcgam(adultdata,'salary','Interactions','all','MaxPValue',0.05)

Mdl = 
  ClassificationGAM
           PredictorNames: {'age'  'workClass'  'fnlwgt'  'education'  'education_num'  'marital_status'  'occupation'  'relationship'  'race'  'sex'  'capital_gain'  'capital_loss'  'hours_per_week'  'native_country'}
             ResponseName: 'salary'
    CategoricalPredictors: [2 4 6 7 8 9 10 14]
               ClassNames: [<=50K    >50K]
           ScoreTransform: 'logit'
                Intercept: -28.5594
             Interactions: [82×2 double]
          NumObservations: 500


  Properties, Methods

Mdl is a ClassificationGAM model object. Mdl includes 82 interaction terms.

Estimate the training sample margins and edge for Mdl.

M = resubMargin(Mdl);
E = resubEdge(Mdl)

E = 
1.0000

Estimate the training sample margins and edge for Mdl without including interaction terms.

M_nointeractions = resubMargin(Mdl,'IncludeInteractions',false);
E_nointeractions = resubEdge(Mdl,'IncludeInteractions',false)

E_nointeractions = 
0.9516

Display the distributions of the margins using box plots.

boxplot([M M_nointeractions],'Labels',{'Linear and Interaction Terms','Linear Terms Only'})
title('Box Plots of Training Sample Margins')

When you include the interaction terms in the computation, all the resubstitution margin values for Mdl are 1, and the resubstitution edge value (average of the margins) is 1. The margins and edge decrease when you do not include the interaction terms in Mdl.

Input Arguments

Mdl — Classification machine learning model

full classification model object

Classification machine learning model, specified as a full classification model object, as given in the following table of supported models.

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

includeInteractions — Flag to include interaction terms

true | false

Flag to include interaction terms of the model, specified as true or false. This argument is valid only for a generalized additive model (GAM). That is, you can specify this argument only when Mdl is ClassificationGAM.

The default value is true if Mdl contains interaction terms. The value must be false if the model does not contain interaction terms.

Data Types: logical

More About

Classification Margin

The classification margin for binary classification is, for each observation, the difference between the classification score for the true class and the classification score for the false class. The classification margin for multiclass classification is the difference between the classification score for the true class and the maximal classification score for the false classes.

If the margins are on the same scale (that is, the score values are based on the same score transformation), then they serve as a classification confidence measure. Among multiple classifiers, those that yield greater margins are better.

Algorithms

resubMargin computes the classification margin according to the corresponding margin function of the object (Mdl). For a model-specific description, see the margin function reference pages in the following table.

Model	Classification Model Object (Mdl)	margin Object Function
Generalized additive model	ClassificationGAM	margin
k-nearest neighbor model	ClassificationKNN	margin
Naive Bayes model	ClassificationNaiveBayes	margin
Neural network model	ClassificationNeuralNetwork	margin
Support vector machine for one-class and binary classification	ClassificationSVM	margin

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 ClassificationKNN, ClassificationNeuralNetwork, or ClassificationSVM object.

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

Version History

Introduced in R2012a

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

resubMargin fully supports GPU arrays for ClassificationNeuralNetwork.

R2023b: Observations with missing predictor values are used in resubstitution and cross-validation computations

Starting in R2023b, the following classification model object functions use observations with missing predictor values as part of resubstitution ("resub") and cross-validation ("kfold") computations for classification edges, losses, margins, and predictions.

Model Type	Model Objects	Object Functions
Discriminant analysis classification model	ClassificationDiscriminant	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedModel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Ensemble of discriminant analysis learners for classification	ClassificationEnsemble	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedEnsemble	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Gaussian kernel classification model	ClassificationPartitionedKernel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
	ClassificationPartitionedKernelECOC	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Linear classification model	ClassificationPartitionedLinear	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
	ClassificationPartitionedLinearECOC	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Neural network classification model	ClassificationNeuralNetwork	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedModel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Support vector machine (SVM) classification model	ClassificationSVM	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedModel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict

In previous releases, the software omitted observations with missing predictor values from the resubstitution and cross-validation computations.