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Resubstitution classification edge



    e = resubEdge(Mdl) returns the weighted resubstitution Classification Edge (e) for the trained classification model Mdl using the predictor data stored in Mdl.X, the corresponding true class labels stored in Mdl.Y, and the observation weights stored in Mdl.W.


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


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    Load the ionosphere data set. This data set has 34 predictors and 351 binary responses for radar returns, either bad ('b') or good ('g').

    load ionosphere

    Train a support vector machine (SVM) classifier. Standardize the data and specify that 'g' is the positive class.

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

    SVMModel is a trained ClassificationSVM classifier.

    Estimate the resubstitution edge, which is the mean of the training sample margins.

    e = resubEdge(SVMModel)
    e = 5.0999

    The classifier edge measures the average of the classifier margins. One way to perform feature selection is to compare training sample edges from multiple models. Based solely on this criterion, the classifier with the highest edge is the best classifier.

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

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

    Define these two data sets:

    • fullX contains all predictors.

    • partX contains the 10 most important predictors.

    fullX = X;
    idx = fscmrmr(X,Y);
    partX = X(:,idx(1:10));

    Train a naive Bayes classifier for each predictor set.

    FullMdl = fitcnb(fullX,Y);
    PartMdl = fitcnb(partX,Y);

    FullMdl and PartMdl are trained ClassificationNaiveBayes classifiers.

    Estimate the training sample edge for each classifier.

    fullEdge = resubEdge(FullMdl)
    fullEdge = 0.6554
    partEdge = resubEdge(PartMdl)
    partEdge = 0.7796

    The edge of the classifier trained on the 10 most important predictors is larger. This result suggests that the classifier trained using only those predictors has a better in-sample fit.

    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 = 
               PredictorNames: {1x14 cell}
                 ResponseName: 'salary'
        CategoricalPredictors: [2 4 6 7 8 9 10 14]
                   ClassNames: [<=50K    >50K]
               ScoreTransform: 'logit'
                    Intercept: -32.0842
                 Interactions: [82x2 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')

    Figure contains an axes. The axes with title Box Plots of Training Sample Margins contains 14 objects of type line.

    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

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

    ModelClassification Model Object
    Generalized additive modelClassificationGAM
    k-nearest neighbor modelClassificationKNN
    Naive Bayes modelClassificationNaiveBayes
    Neural network modelClassificationNeuralNetwork
    Support vector machine for one-class and binary classificationClassificationSVM

    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

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    Classification Edge

    The classification edge is the weighted mean of the classification margins.

    One way to choose among multiple classifiers, for example to perform feature selection, is to choose the classifier that yields the greatest edge.

    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.


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

    ModelClassification Model Object (Mdl)edge Object Function
    Generalized additive modelClassificationGAMedge
    k-nearest neighbor modelClassificationKNNedge
    Naive Bayes modelClassificationNaiveBayesedge
    Neural network modelClassificationNeuralNetworkedge
    Support vector machine for one-class and binary classificationClassificationSVMedge

    Extended Capabilities

    Introduced in R2012a