ClassificationDiscriminant class

Superclasses: CompactClassificationDiscriminant

Discriminant analysis classification


A ClassificationDiscriminant object encapsulates a discriminant analysis classifier, which is a Gaussian mixture model for data generation. A ClassificationDiscriminant object can predict responses for new data using the predict method. The object contains the data used for training, so can compute resubstitution predictions.


obj = fitcdiscr(x,Y) creates a discriminant classification object based on the input variables (also known as predictors, features, or attributes) x and output (response) Y. For syntax details, see fitcdiscr.

obj = fitcdiscr(x,Y,Name,Value) creates a classifier with additional options specified by one or more Name,Value pair arguments. If you use one of the following five options, obj is of class ClassificationPartitionedModel: 'CrossVal', 'KFold', 'Holdout', 'Leaveout', or 'CVPartition'. Otherwise, obj is of class ClassificationDiscriminant.

Input Arguments


Matrix of numeric predictor values. Each column of x represents one variable, and each row represents one observation.

NaN values in x are considered missing values. Observations with missing values for x are not used in the fit.


A categorical array, cell array of strings, character array, logical vector, or a numeric vector with the same number of rows as x. Each row of Y represents the classification of the corresponding row of x. NaN values in Y are considered missing values. Observations with missing values for Y are not used in the fit.



p-by-p matrix, the between-class covariance, where p is the number of predictors.


List of categorical predictors, which is always empty ([]) for SVM and discriminant analysis classifiers.


List of the elements in the training data Y with duplicates removed. ClassNames can be a categorical array, cell array of strings, character array, logical vector, or a numeric vector. ClassNames has the same data type as the data in the argument Y.


k-by-k structure of coefficient matrices, where k is the number of classes. Coeffs(i,j) contains coefficients of the linear or quadratic boundaries between classes i and j. Fields in Coeffs(i,j):

  • DiscrimType

  • Class1ClassNames(i)

  • Class2ClassNames(j)

  • Const — A scalar

  • Linear — A vector with p components, where p is the number of columns in X

  • Quadraticp-by-p matrix, exists for quadratic DiscrimType

The equation of the boundary between class i and class j is

Const + Linear * x + x' * Quadratic * x = 0,

where x is a column vector of length p.

If fitcdiscr had the FillCoeffs name-value pair set to 'off' when constructing the classifier, Coeffs is empty ([]).


Square matrix, where Cost(i,j) is the cost of classifying a point into class j if its true class is i (i.e., the rows correspond to the true class and the columns correspond to the predicted class). The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames. The number of rows and columns in Cost is the number of unique classes in the response.

Change a Cost matrix using dot notation: obj.Cost = costMatrix.


Value of the Delta threshold for a linear discriminant model, a nonnegative scalar. If a coefficient of obj has magnitude smaller than Delta, obj sets this coefficient to 0, and so you can eliminate the corresponding predictor from the model. Set Delta to a higher value to eliminate more predictors.

Delta must be 0 for quadratic discriminant models.

Change Delta using dot notation: obj.Delta = newDelta.


Row vector of length equal to the number of predictors in obj. If DeltaPredictor(i) < Delta then coefficient i of the model is 0.

If obj is a quadratic discriminant model, all elements of DeltaPredictor are 0.


String specifying the discriminant type. One of:

  • 'linear'

  • 'quadratic'

  • 'diagLinear'

  • 'diagQuadratic'

  • 'pseudoLinear'

  • 'pseudoQuadratic'

Change DiscrimType using dot notation: obj.DiscrimType = newDiscrimType.

You can change between linear types, or between quadratic types, but cannot change between linear and quadratic types.


Value of the Gamma regularization parameter, a scalar from 0 to 1. Change Gamma using dot notation: obj.Gamma = newGamma.

  • If you set 1 for linear discriminant, the discriminant sets its type to 'diagLinear'.

  • If you set a value between MinGamma and 1 for linear discriminant, the discriminant sets its type to 'linear'.

  • You cannot set values below the value of the MinGamma property.

  • For quadratic discriminant, you can set either 0 (for DiscrimType 'quadratic') or 1 (for DiscrimType 'diagQuadratic').


Logarithm of the determinant of the within-class covariance matrix. The type of LogDetSigma depends on the discriminant type:

  • Scalar for linear discriminant analysis

  • Vector of length K for quadratic discriminant analysis, where K is the number of classes


Nonnegative scalar, the minimal value of the Gamma parameter so that the correlation matrix is invertible. If the correlation matrix is not singular, MinGamma is 0.


Parameters used in training obj.


Class means, specified as a K-by-p matrix of scalar values class means of size. K is the number of classes, and p is the number of predictors. Each row of Mu represents the mean of the multivariate normal distribution of the corresponding class. The class indices are in the ClassNames attribute.


Number of observations in the training data, a numeric scalar. NumObservations can be less than the number of rows of input data X when there are missing values in X or response Y.


Cell array of names for the predictor variables, in the order in which they appear in the training data X.


Numeric vector of prior probabilities for each class. The order of the elements of Prior corresponds to the order of the classes in ClassNames.

Add or change a Prior vector using dot notation: obj.Prior = priorVector.


String describing the response variable Y.


Function handle for transforming scores, or string representing a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x. For a list of built-in transformation functions and the syntax of custom transformation functions, see fitcdiscr.

Implement dot notation to add or change a ScoreTransform function using one of the following:

  • cobj.ScoreTransform = 'function'

  • cobj.ScoreTransform = @function


Within-class covariance matrix or matrices. The dimensions depend on DiscrimType:

  • 'linear' (default) — Matrix of size p-by-p, where p is the number of predictors

  • 'quadratic' — Array of size p-by-p-by-K, where K is the number of classes

  • 'diagLinear' — Row vector of length p

  • 'diagQuadratic' — Array of size 1-by-p-by-K

  • 'pseudoLinear' — Matrix of size p-by-p

  • 'pseudoQuadratic' — Array of size p-by-p-by-K


Scaled weights, a vector with length n, the number of rows in X.


Matrix of predictor values. Each column of X represents one predictor (variable), and each row represents one observation.


X data with class means subtracted. If Y(i) is of class j,

Xcentered(i,:) = X(i,:)Mu(j,:),

where Mu is the class mean property.


A categorical array, cell array of strings, character array, logical vector, or a numeric vector with the same number of rows as X. Each row of Y represents the classification of the corresponding row of X.


compactCompact discriminant analysis classifier
crossvalCross-validated discriminant analysis classifier
cvshrinkCross-validate regularization of linear discriminant
resubEdgeClassification edge by resubstitution
resubLossClassification error by resubstitution
resubMarginClassification margins by resubstitution
resubPredictPredict resubstitution response of classifier

Inherited Methods

compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge
logPLog unconditional probability density for discriminant analysis classifier
lossClassification error
mahalMahalanobis distance to class means
marginClassification margins
nLinearCoeffsNumber of nonzero linear coefficients
predictPredict classification


Discriminant Classification

The model for discriminant analysis is:

  • Each class (Y) generates data (X) using a multivariate normal distribution. That is, the model assumes X has a Gaussian mixture distribution (gmdistribution).

    • For linear discriminant analysis, the model has the same covariance matrix for each class, only the means vary.

    • For quadratic discriminant analysis, both means and covariances of each class vary.

predict classifies so as to minimize the expected classification cost:



  • y^ is the predicted classification.

  • K is the number of classes.

  • P^(k|x) is the posterior probability of class k for observation x.

  • C(y|k) is the cost of classifying an observation as y when its true class is k.

For details, see How the predict Method Classifies.


Regularization is the process of finding a small set of predictors that yield an effective predictive model. For linear discriminant analysis, there are two parameters, γ and δ, that control regularization as follows. cvshrink helps you select appropriate values of the parameters.

Let Σ represent the covariance matrix of the data X, and let X^ be the centered data (the data X minus the mean by class). Define


The regularized covariance matrix Σ˜ is


Whenever γ ≥ MinGamma, Σ˜ is nonsingular.

Let μk be the mean vector for those elements of X in class k, and let μ0 be the global mean vector (the mean of the rows of X). Let C be the correlation matrix of the data X, and let C˜ be the regularized correlation matrix:


where I is the identity matrix.

The linear term in the regularized discriminant analysis classifier for a data point x is


The parameter δ enters into this equation as a threshold on the final term in square brackets. Each component of the vector [C˜1D1/2(μkμ0)] is set to zero if it is smaller in magnitude than the threshold δ. Therefore, for class k, if component j is thresholded to zero, component j of x does not enter into the evaluation of the posterior probability.

The DeltaPredictor property is a vector related to this threshold. When δ ≥ DeltaPredictor(i), all classes k have


Therefore, when δ ≥ DeltaPredictor(i), the regularized classifier does not use predictor i.

Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB® documentation.


Create a discriminant analysis classifier for the Fisher iris data:

load fisheriris
obj = fitcdiscr(meas,species)

obj = 
    PredictorNames: {'x1'  'x2'  'x3'  'x4'}
      ResponseName: 'Y'
        ClassNames: {'setosa'  'versicolor'  'virginica'}
    ScoreTransform: 'none'
     NumObservations: 150
       DiscrimType: 'linear'
                Mu: [3x4 double]
            Coeffs: [3x3 struct]


[1] Guo, Y., T. Hastie, and R. Tibshirani. Regularized linear discriminant analysis and its application in microarrays. Biostatistics, Vol. 8, No. 1, pp. 86–100, 2007.

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