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Class: ClassificationDiscriminant

Classification error by resubstitution


L = resubLoss(obj)
L = resubLoss(obj,Name,Value)


L = resubLoss(obj) returns the resubstitution loss, meaning the loss computed for the data that fitcdiscr used to create obj.

L = resubLoss(obj,Name,Value) returns loss statistics with additional options specified by one or more Name,Value pair arguments.

Input Arguments

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Discriminant analysis classifier, produced using fitcdiscr.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Loss function, specified as the comma-separated pair consisting of 'LossFun' and a built-in loss function name or function handle.

  • The following table lists the available loss functions. Specify one using the corresponding character vector or string scalar.

    'binodeviance'Binomial deviance
    'classifcost'Observed misclassification cost
    'classiferror'Misclassified rate in decimal
    'exponential'Exponential loss
    'hinge'Hinge loss
    'logit'Logistic loss
    'mincost'Minimal expected misclassification cost (for classification scores that are posterior probabilities)
    'quadratic'Quadratic loss

    'mincost' is appropriate for classification scores that are posterior probabilities. Discriminant analysis models return posterior probabilities as classification scores by default (see predict).

  • Specify your own function using function handle notation.

    Suppose that n be the number of observations in X and K be the number of distinct classes (numel(obj.ClassNames)). Your function must have this signature

    lossvalue = lossfun(C,S,W,Cost)

    • The output argument lossvalue is a scalar.

    • You choose the function name (lossfun).

    • C is an n-by-K logical matrix with rows indicating which class the corresponding observation belongs. The column order corresponds to the class order in obj.ClassNames.

      Construct C by setting C(p,q) = 1 if observation p is in class q, for each row. Set all other elements of row p to 0.

    • S is an n-by-K numeric matrix of classification scores. The column order corresponds to the class order in obj.ClassNames. S is a matrix of classification scores, similar to the output of predict.

    • W is an n-by-1 numeric vector of observation weights. If you pass W, the software normalizes them to sum to 1.

    • Cost is a K-by-K numeric matrix of misclassification costs. For example, Cost = ones(K) - eye(K) specifies a cost of 0 for correct classification, and 1 for misclassification.

    Specify your function using 'LossFun',@lossfun.

For more details on loss functions, see Classification Loss.

Data Types: char | string | function_handle

Output Arguments


Classification error, a scalar. The meaning of the error depends on the values in weights and lossfun. See Classification Loss.


Compute the resubstituted classification error for the Fisher iris data:

load fisheriris
obj = fitcdiscr(meas,species);
L = resubLoss(obj)

L =

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