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Resubstitution classification loss for discriminant analysis classifier



L = resubLoss(Mdl) returns the Classification Loss L by resubstitution for the trained discriminant analysis classifier Mdl using the training data stored in Mdl.X and the corresponding true class labels stored in Mdl.Y. By default, resubLoss uses the loss, meaning the loss computed for the data used by fitcdiscr to create Mdl.

L = resubLoss(___,LossFun=lossf) returns the resubstitution loss using a built-in or custom loss function.

The classification loss (L) is a resubstitution quality measure, and is returned as a numeric scalar. Its interpretation depends on the loss function (lossf), but in general, better classifiers yield smaller classification loss values.


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Compute the resubstituted classification error for the Fisher iris data.

Create a classification model for the Fisher iris data.

load fisheriris
mdl = fitcdiscr(meas,species);

Compute the resubstituted classification error.

L = resubLoss(mdl)
L =

Input Arguments

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Discriminant analysis classifier, specified as a ClassificationDiscriminant model object trained with fitcdiscr.

Loss function, specified as a built-in loss function name or a function handle.

The following table describes the values for the built-in 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 classifiers return posterior probabilities as classification scores by default (see predict).

Specify your own function using function handle notation. Suppose that n is the number of observations in X, and K is the number of distinct classes (numel(Mdl.ClassNames)). Your function must have the signature

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

  • The output argument lossvalue is a scalar.

  • You specify the function name (lossfun).

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

    Create 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 Mdl.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 the weights 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.

Example: LossFun="binodeviance"

Example: LossFun=@lossf

Data Types: char | string | function_handle

More About

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Classification Loss

Classification loss functions measure the predictive inaccuracy of classification models. When you compare the same type of loss among many models, a lower loss indicates a better predictive model.

Consider the following scenario.

  • L is the weighted average classification loss.

  • n is the sample size.

  • For binary classification:

    • yj is the observed class label. The software codes it as –1 or 1, indicating the negative or positive class (or the first or second class in the ClassNames property), respectively.

    • f(Xj) is the positive-class classification score for observation (row) j of the predictor data X.

    • mj = yjf(Xj) is the classification score for classifying observation j into the class corresponding to yj. Positive values of mj indicate correct classification and do not contribute much to the average loss. Negative values of mj indicate incorrect classification and contribute significantly to the average loss.

  • For algorithms that support multiclass classification (that is, K ≥ 3):

    • yj* is a vector of K – 1 zeros, with 1 in the position corresponding to the true, observed class yj. For example, if the true class of the second observation is the third class and K = 4, then y2* = [0 0 1 0]′. The order of the classes corresponds to the order in the ClassNames property of the input model.

    • f(Xj) is the length K vector of class scores for observation j of the predictor data X. The order of the scores corresponds to the order of the classes in the ClassNames property of the input model.

    • mj = yj*f(Xj). Therefore, mj is the scalar classification score that the model predicts for the true, observed class.

  • The weight for observation j is wj. The software normalizes the observation weights so that they sum to the corresponding prior class probability stored in the Prior property. Therefore,


Given this scenario, the following table describes the supported loss functions that you can specify by using the LossFun name-value argument.

Loss FunctionValue of LossFunEquation
Binomial deviance"binodeviance"L=j=1nwjlog{1+exp[2mj]}.
Observed misclassification cost"classifcost"


where y^j is the class label corresponding to the class with the maximal score, and cyjy^j is the user-specified cost of classifying an observation into class y^j when its true class is yj.

Misclassified rate in decimal"classiferror"


where I{·} is the indicator function.

Cross-entropy loss"crossentropy"

"crossentropy" is appropriate only for neural network models.

The weighted cross-entropy loss is


where the weights w˜j are normalized to sum to n instead of 1.

Exponential loss"exponential"L=j=1nwjexp(mj).
Hinge loss"hinge"L=j=1nwjmax{0,1mj}.
Logit loss"logit"L=j=1nwjlog(1+exp(mj)).
Minimal expected misclassification cost"mincost"

"mincost" is appropriate only if classification scores are posterior probabilities.

The software computes the weighted minimal expected classification cost using this procedure for observations j = 1,...,n.

  1. Estimate the expected misclassification cost of classifying the observation Xj into the class k:


    f(Xj) is the column vector of class posterior probabilities for the observation Xj. C is the cost matrix stored in the Cost property of the model.

  2. For observation j, predict the class label corresponding to the minimal expected misclassification cost:


  3. Using C, identify the cost incurred (cj) for making the prediction.

The weighted average of the minimal expected misclassification cost loss is


Quadratic loss"quadratic"L=j=1nwj(1mj)2.

If you use the default cost matrix (whose element value is 0 for correct classification and 1 for incorrect classification), then the loss values for "classifcost", "classiferror", and "mincost" are identical. For a model with a nondefault cost matrix, the "classifcost" loss is equivalent to the "mincost" loss most of the time. These losses can be different if prediction into the class with maximal posterior probability is different from prediction into the class with minimal expected cost. Note that "mincost" is appropriate only if classification scores are posterior probabilities.

This figure compares the loss functions (except "classifcost", "crossentropy", and "mincost") over the score m for one observation. Some functions are normalized to pass through the point (0,1).

Comparison of classification losses for different loss functions

Posterior Probability

The posterior probability that a point x belongs to class k is the product of the prior probability and the multivariate normal density. The density function of the multivariate normal with 1-by-d mean μk and d-by-d covariance Σk at a 1-by-d point x is


where |Σk| is the determinant of Σk, and Σk1 is the inverse matrix.

Let P(k) represent the prior probability of class k. Then the posterior probability that an observation x is of class k is


where P(x) is a normalization constant, the sum over k of P(x|k)P(k).

Prior Probability

The prior probability is one of three choices:

  • 'uniform' — The prior probability of class k is one over the total number of classes.

  • 'empirical' — The prior probability of class k is the number of training samples of class k divided by the total number of training samples.

  • Custom — The prior probability of class k is the kth element of the prior vector. See fitcdiscr.

After creating a classification model (Mdl) you can set the prior using dot notation:

Mdl.Prior = v;

where v is a vector of positive elements representing the frequency with which each element occurs. You do not need to retrain the classifier when you set a new prior.


The matrix of expected costs per observation is defined in Cost.

Version History

Introduced in R2011b

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