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## Discriminant Analysis

### What Is Discriminant Analysis?

Discriminant analysis is a classification method. It assumes that different classes generate data based on different Gaussian distributions.

Linear discriminant analysis is also known as the Fisher discriminant, named for its inventor, Sir R. A. Fisher [2].

### Create Discriminant Analysis Classifiers

This example shows how to train a basic discriminant analysis classifier to classify irises in Fisher's iris data.

load fisheriris 

Create a default (linear) discriminant analysis classifier.

MdlLinear = fitcdiscr(meas,species); 

To visualize the classification boundaries of a 2-D linear classification of the data, see Create and Visualize Discriminant Analysis Classifier.

Classify an iris with average measurements.

meanmeas = mean(meas); meanclass = predict(MdlLinear,meanmeas) 
meanclass = cell 'versicolor' 

MdlQuadratic = fitcdiscr(meas,species,'DiscrimType','quadratic'); 

To visualize the classification boundaries of a 2-D quadratic classification of the data, see Create and Visualize Discriminant Analysis Classifier.

Classify an iris with average measurements using the quadratic classifier.

meanclass2 = predict(MdlQuadratic,meanmeas) 
meanclass2 = cell 'versicolor' 

### Creating a Classifier Using fitcdiscr

The model for discriminant analysis is:

• Each class (Y) generates data (X) using a multivariate normal distribution. In other words, 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.

Under this modeling assumption, fitcdiscr infers the mean and covariance parameters of each class.

• For linear discriminant analysis, it computes the sample mean of each class. Then it computes the sample covariance by first subtracting the sample mean of each class from the observations of that class, and taking the empirical covariance matrix of the result.

• For quadratic discriminant analysis, it computes the sample mean of each class. Then it computes the sample covariances by first subtracting the sample mean of each class from the observations of that class, and taking the empirical covariance matrix of each class.

The fit method does not use prior probabilities or costs for fitting.

#### Weighted Observations

fitcdiscr constructs weighted classifiers using the following scheme. Suppose M is an N-by-K class membership matrix:

Mnk = 1 if observation n is from class k
Mnk = 0 otherwise.

The estimate of the class mean for unweighted data is

${\stackrel{^}{\mu }}_{k}=\frac{{\sum }_{n=1}^{N}{M}_{nk}{x}_{n}}{{\sum }_{n=1}^{N}{M}_{nk}}.$

For weighted data with positive weights wn, the natural generalization is

${\stackrel{^}{\mu }}_{k}=\frac{{\sum }_{n=1}^{N}{M}_{nk}{w}_{n}{x}_{n}}{{\sum }_{n=1}^{N}{M}_{nk}{w}_{n}}.$

The unbiased estimate of the pooled-in covariance matrix for unweighted data is

$\stackrel{^}{\Sigma }=\frac{{\sum }_{n=1}^{N}{\sum }_{k=1}^{K}{M}_{nk}\left({x}_{n}-{\stackrel{^}{\mu }}_{k}\right){\left({x}_{n}-{\stackrel{^}{\mu }}_{k}\right)}^{T}}{N-K}.$

For quadratic discriminant analysis, fitcdiscr uses K = 1.

For weighted data, assuming the weights sum to 1, the unbiased estimate of the pooled-in covariance matrix is

$\stackrel{^}{\Sigma }=\frac{{\sum }_{n=1}^{N}{\sum }_{k=1}^{K}{M}_{nk}{w}_{n}\left({x}_{n}-{\stackrel{^}{\mu }}_{k}\right){\left({x}_{n}-{\stackrel{^}{\mu }}_{k}\right)}^{T}}{1-{\sum }_{k=1}^{K}\frac{{W}_{k}^{\left(2\right)}}{{W}_{k}}},$

where

• ${W}_{k}={\sum }_{n=1}^{N}{M}_{nk}{w}_{n}$ is the sum of the weights for class k.

• ${W}_{k}^{\left(2\right)}={\sum }_{n=1}^{N}{M}_{nk}{w}_{n}^{2}$ is the sum of squared weights per class.

### How the predict Method Classifies

predict uses three quantities to classify observations: posterior probability, prior probability, and cost.

predict classifies so as to minimize the expected classification cost:

$\stackrel{^}{y}=\underset{y=1,...,K}{\mathrm{arg}\mathrm{min}}\sum _{k=1}^{K}\stackrel{^}{P}\left(k|x\right)C\left(y|k\right),$

where

• $\stackrel{^}{y}$ is the predicted classification.

• K is the number of classes.

• $\stackrel{^}{P}\left(k|x\right)$ is the posterior probability of class k for observation x.

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

The space of X values divides into regions where a classification Y is a particular value. The regions are separated by straight lines for linear discriminant analysis, and by conic sections (ellipses, hyperbolas, or parabolas) for quadratic discriminant analysis. For a visualization of these regions, see Create and Visualize Discriminant Analysis Classifier.

#### 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 mean μk and covariance Σk at a point x is

$P\left(x|k\right)=\frac{1}{{\left(2\pi |{\Sigma }_{k}|\right)}^{1/2}}\mathrm{exp}\left(-\frac{1}{2}{\left(x-{\mu }_{k}\right)}^{T}{\Sigma }_{k}^{-1}\left(x-{\mu }_{k}\right)\right),$

where $|{\Sigma }_{k}|$ is the determinant of Σk, and ${\Sigma }_{k}^{-1}$ 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

$\stackrel{^}{P}\left(k|x\right)=\frac{P\left(x|k\right)P\left(k\right)}{P\left(x\right)},$

where P(x) is a normalization constant, namely, 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 1 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.

• A numeric vector — The prior probability of class k is the jth element of the Prior vector. See fitcdiscr.

After creating a classifier obj, you can set the prior using dot notation:

obj.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.

#### Cost

There are two costs associated with discriminant analysis classification: the true misclassification cost per class, and the expected misclassification cost per observation.

True Misclassification Cost per Class.  Cost(i,j) is the cost of classifying an observation into class j if its true class is i. By default, Cost(i,j)=1 if i~=j, and Cost(i,j)=0 if i=j. In other words, the cost is 0 for correct classification, and 1 for incorrect classification.

You can set any cost matrix you like when creating a classifier. Pass the cost matrix in the Cost name-value pair in fitcdiscr.

After you create a classifier obj, you can set a custom cost using dot notation:

obj.Cost = B;

B is a square matrix of size K-by-K when there are K classes. You do not need to retrain the classifier when you set a new cost.

Expected Misclassification Cost per Observation.  Suppose you have Nobs observations that you want to classify with a trained discriminant analysis classifier obj. Suppose you have K classes. You place the observations into a matrix Xnew with one observation per row. The command

[label,score,cost] = predict(obj,Xnew)

returns, among other outputs, a cost matrix of size Nobs-by-K. Each row of the cost matrix contains the expected (average) cost of classifying the observation into each of the K classes. cost(n,k) is

$\sum _{i=1}^{K}\stackrel{^}{P}\left(i|Xnew\left(n\right)\right)C\left(k|i\right),$

where

• K is the number of classes.

• $\stackrel{^}{P}\left(i|Xnew\left(n\right)\right)$ is the posterior probability of class i for observation Xnew(n).

• $C\left(k|i\right)$ is the cost of classifying an observation as k when its true class is i.

### Create and Visualize Discriminant Analysis Classifier

This example shows how to perform linear and quadratic classification of Fisher iris data.

load fisheriris 

The column vector, species , consists of iris flowers of three different species, setosa, versicolor, virginica. The double matrix meas consists of four types of measurements on the flowers, the length and width of sepals and petals in centimeters, respectively.

Use petal length (third column in meas ) and petal width (fourth column in meas ) measurements. Save these as variables PL and PW, respectively.

PL = meas(:,3); PW = meas(:,4); 

Plot the data, showing the classification, that is, create a scatter plot of the measurements, grouped by species.

h1 = gscatter(PL,PW,species,'krb','ov^',[],'off'); h1(1).LineWidth = 2; h1(2).LineWidth = 2; h1(3).LineWidth = 2; legend('Setosa','Versicolor','Virginica','Location','best') hold on 

Create a linear classifier.

X = [PL,PW]; MdlLinear = fitcdiscr(X,species); 

Retrieve the coefficients for the linear boundary between the second and third classes.

MdlLinear.ClassNames([2 3]) K = MdlLinear.Coeffs(2,3).Const; L = MdlLinear.Coeffs(2,3).Linear; 
ans = 2×1 cell array 'versicolor' 'virginica' 

Plot the curve that separates the second and third classes

 
f = @(x1,x2) K + L(1)*x1 + L(2)*x2; h2 = ezplot(f,[.9 7.1 0 2.5]); h2.Color = 'r'; h2.LineWidth = 2; 

Retrieve the coefficients for the linear boundary between the first and second classes.

MdlLinear.ClassNames([1 2]) K = MdlLinear.Coeffs(1,2).Const; L = MdlLinear.Coeffs(1,2).Linear; 
ans = 2×1 cell array 'setosa' 'versicolor' 

Plot the curve that separates the first and second classes.

f = @(x1,x2) K + L(1)*x1 + L(2)*x2; h3 = ezplot(f,[.9 7.1 0 2.5]); h3.Color = 'k'; h3.LineWidth = 2; axis([.9 7.1 0 2.5]) xlabel('Petal Length') ylabel('Petal Width') title('{\bf Linear Classification with Fisher Training Data}') 

MdlQuadratic = fitcdiscr(X,species,'DiscrimType','quadratic'); 

Remove the linear boundaries from the plot.

delete(h2); delete(h3); 

Retrieve the coefficients for the quadratic boundary between the second and third classes.

MdlQuadratic.ClassNames([2 3]) K = MdlQuadratic.Coeffs(2,3).Const; L = MdlQuadratic.Coeffs(2,3).Linear; Q = MdlQuadratic.Coeffs(2,3).Quadratic; 
ans = 2×1 cell array 'versicolor' 'virginica' 

Plot the curve that separates the second and third classes

 
f = @(x1,x2) K + L(1)*x1 + L(2)*x2 + Q(1,1)*x1.^2 + ... (Q(1,2)+Q(2,1))*x1.*x2 + Q(2,2)*x2.^2; h2 = ezplot(f,[.9 7.1 0 2.5]); h2.Color = 'r'; h2.LineWidth = 2; 

Retrieve the coefficients for the quadratic boundary between the first and second classes.

MdlQuadratic.ClassNames([1 2]) K = MdlQuadratic.Coeffs(1,2).Const; L = MdlQuadratic.Coeffs(1,2).Linear; Q = MdlQuadratic.Coeffs(1,2).Quadratic; 
ans = 2×1 cell array 'setosa' 'versicolor' 

Plot the curve that separates the first and second and classes.

f = @(x1,x2) K + L(1)*x1 + L(2)*x2 + Q(1,1)*x1.^2 + ... (Q(1,2)+Q(2,1))*x1.*x2 + Q(2,2)*x2.^2; h3 = ezplot(f,[.9 7.1 0 1.02]); % Plot the relevant portion of the curve. h3.Color = 'k'; h3.LineWidth = 2; axis([.9 7.1 0 2.5]) xlabel('Petal Length') ylabel('Petal Width') title('{\bf Quadratic Classification with Fisher Training Data}') hold off 

### Improve a Discriminant Analysis Classifier

#### Deal with Singular Data

Discriminant analysis needs data sufficient to fit Gaussian models with invertible covariance matrices. If your data is not sufficient to fit such a model uniquely, fitcdiscr fails. This section shows methods for handling failures.

 Tip   To obtain a discriminant analysis classifier without failure, set the DiscrimType name-value pair to 'pseudoLinear' or 'pseudoQuadratic' in fitcdiscr."Pseudo" discriminants never fail, because they use the pseudoinverse of the covariance matrix Σk (see pinv).

Example: Singular Covariance Matrix.  When the covariance matrix of the fitted classifier is singular, fitcdiscr can fail:

load popcorn X = popcorn(:,[1 2]); X(:,3) = 0; % a zero-variance column Y = popcorn(:,3); ppcrn = fitcdiscr(X,Y); Error using ClassificationDiscriminant (line 635) Predictor x3 has zero variance. Either exclude this predictor or set 'discrimType' to 'pseudoLinear' or 'diagLinear'. Error in classreg.learning.FitTemplate/fit (line 243) obj = this.MakeFitObject(X,Y,W,this.ModelParameters,fitArgs{:}); Error in fitcdiscr (line 296) this = fit(temp,X,Y);

To proceed with linear discriminant analysis, use a pseudoLinear or diagLinear discriminant type:

ppcrn = fitcdiscr(X,Y,... 'discrimType','pseudoLinear'); meanpredict = predict(ppcrn,mean(X)) meanpredict = 3.5000

#### Choose a Discriminant Type

There are six types of discriminant analysis classifiers: linear and quadratic, with diagonal and pseudo variants of each type.

 Tip   To see if your covariance matrix is singular, set discrimType to 'linear' or 'quadratic'. If the matrix is singular, the fitcdiscr method fails for 'quadratic', and the Gamma property is nonzero for 'linear'.To obtain a quadratic classifier even when your covariance matrix is singular, set DiscrimType to 'pseudoQuadratic' or 'diagQuadratic'.obj = fitcdiscr(X,Y,'DiscrimType','pseudoQuadratic') % or 'diagQuadratic'

Choose a classifier type by setting the discrimType name-value pair to one of:

• 'linear' (default) — Estimate one covariance matrix for all classes.

• 'quadratic' — Estimate one covariance matrix for each class.

• 'diagLinear' — Use the diagonal of the 'linear' covariance matrix, and use its pseudoinverse if necessary.

• 'diagQuadratic' — Use the diagonals of the 'quadratic' covariance matrices, and use their pseudoinverses if necessary.

• 'pseudoLinear' — Use the pseudoinverse of the 'linear' covariance matrix if necessary.

• 'pseudoQuadratic' — Use the pseudoinverses of the 'quadratic' covariance matrices if necessary.

fitcdiscr can fail for the 'linear' and 'quadratic' classifiers. When it fails, it returns an explanation, as shown in Deal with Singular Data.

fitcdiscr always succeeds with the diagonal and pseudo variants. For information about pseudoinverses, see pinv.

You can set the discriminant type using dot notation after constructing a classifier:

obj.DiscrimType = 'discrimType'

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

#### Examine the Resubstitution Error and Confusion Matrix

The resubstitution error is the difference between the response training data and the predictions the classifier makes of the response based on the input training data. If the resubstitution error is high, you cannot expect the predictions of the classifier to be good. However, having low resubstitution error does not guarantee good predictions for new data. Resubstitution error is often an overly optimistic estimate of the predictive error on new data.

The confusion matrix shows how many errors, and which types, arise in resubstitution. When there are K classes, the confusion matrix R is a K-by-K matrix with

R(i,j) = the number of observations of class i that the classifier predicts to be of class j.

Example: Resubstitution Error of a Discriminant Analysis Classifier.  Examine the resubstitution error of the default discriminant analysis classifier for the Fisher iris data:

load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) resuberror = 0.0200

The resubstitution error is very low, meaning obj classifies nearly all the Fisher iris data correctly. The total number of misclassifications is:

resuberror * obj.NumObservations ans = 3.0000

To see the details of the three misclassifications, examine the confusion matrix:

R = confusionmat(obj.Y,resubPredict(obj)) R = 50 0 0 0 48 2 0 1 49 obj.ClassNames ans = 'setosa' 'versicolor' 'virginica'
• R(1,:) = [50 0 0] means obj classifies all 50 setosa irises correctly.

• R(2,:) = [0 48 2] means obj classifies 48 versicolor irises correctly, and misclassifies two versicolor irises as virginica.

• R(3,:) = [0 1 49] means obj classifies 49 virginica irises correctly, and misclassifies one virginica iris as versicolor.

#### Cross Validation

Typically, discriminant analysis classifiers are robust and do not exhibit overtraining when the number of predictors is much less than the number of observations. Nevertheless, it is good practice to cross validate your classifier to ensure its stability.

Cross Validating a Discriminant Analysis Classifier

This example shows how to perform five-fold cross validation of a quadratic discriminant analysis classifier.

load fisheriris 

Create a quadratic discriminant analysis classifier for the data.

quadisc = fitcdiscr(meas,species,'DiscrimType','quadratic'); 

Find the resubstitution error of the classifier.

qerror = resubLoss(quadisc) 
qerror = 0.0200 

The classifier does an excellent job. Nevertheless, resubstitution error can be an optimistic estimate of the error when classifying new data. So proceed to cross validation.

Create a cross-validation model.

cvmodel = crossval(quadisc,'kfold',5); 

Find the cross-validation loss for the model, meaning the error of the out-of-fold observations.

cverror = kfoldLoss(cvmodel) 
cverror = 0.0200 

The cross-validated loss is as low as the original resubstitution loss. Therefore, you can have confidence that the classifier is reasonably accurate.

#### Change Costs and Priors

Sometimes you want to avoid certain misclassification errors more than others. For example, it might be better to have oversensitive cancer detection instead of undersensitive cancer detection. Oversensitive detection gives more false positives (unnecessary testing or treatment). Undersensitive detection gives more false negatives (preventable illnesses or deaths). The consequences of underdetection can be high. Therefore, you might want to set costs to reflect the consequences.

Similarly, the training data Y can have a distribution of classes that does not represent their true frequency. If you have a better estimate of the true frequency, you can include this knowledge in the classification Prior property.

Example: Setting Custom Misclassification Costs.  Consider the Fisher iris data. Suppose that the cost of classifying a versicolor iris as virginica is 10 times as large as making any other classification error. Create a classifier from the data, then incorporate this cost and then view the resulting classifier.

1. Load the Fisher iris data and create a default (linear) classifier as in Example: Resubstitution Error of a Discriminant Analysis Classifier:

load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) resuberror = 0.0200 R = confusionmat(obj.Y,resubPredict(obj)) R = 50 0 0 0 48 2 0 1 49 obj.ClassNames ans = 'setosa' 'versicolor' 'virginica'

R(2,:) = [0 48 2] means obj classifies 48 versicolor irises correctly, and misclassifies two versicolor irises as virginica.

2. Change the cost matrix to make fewer mistakes in classifying versicolor irises as virginica:

obj.Cost(2,3) = 10; R2 = confusionmat(obj.Y,resubPredict(obj)) R2 = 50 0 0 0 50 0 0 7 43

obj now classifies all versicolor irises correctly, at the expense of increasing the number of misclassifications of virginica irises from 1 to 7.

Example: Setting Alternative Priors.  Consider the Fisher iris data. There are 50 irises of each kind in the data. Suppose that, in a particular region, you have historical data that shows virginica are five times as prevalent as the other kinds. Create a classifier that incorporates this information.

1. Load the Fisher iris data and make a default (linear) classifier as in Example: Resubstitution Error of a Discriminant Analysis Classifier:

load fisheriris obj = fitcdiscr(meas,species); resuberror = resubLoss(obj) resuberror = 0.0200 R = confusionmat(obj.Y,resubPredict(obj)) R = 50 0 0 0 48 2 0 1 49 obj.ClassNames ans = 'setosa' 'versicolor' 'virginica'

R(3,:) = [0 1 49] means obj classifies 49 virginica irises correctly, and misclassifies one virginica iris as versicolor.

2. Change the prior to match your historical data, and examine the confusion matrix of the new classifier:

obj.Prior = [1 1 5]; R2 = confusionmat(obj.Y,resubPredict(obj)) R2 = 50 0 0 0 46 4 0 0 50

The new classifier classifies all virginica irises correctly, at the expense of increasing the number of misclassifications of versicolor irises from 2 to 4.

### Regularize a Discriminant Analysis Classifier

This example shows how to make a more robust and simpler model by trying to remove predictors without hurting the predictive power of the model. This is especially important when you have many predictors in your data. Linear discriminant analysis uses the two regularization parameters, Gamma and Delta (see Definitions), to identify and remove redundant predictors. The ClassificationDiscriminant.cvshrink method helps identify appropriate settings for these parameters.

Load data and create a classifier.

Create a linear discriminant analysis classifier for the ovariancancer data. Set the SaveMemory and FillCoeffs name-value pair arguments to keep the resulting model reasonably small. For computational ease, this example uses a random subset of about one third of the predictors to train the classifier.

load ovariancancer rng(1); % For reproducibility numPred = size(obs,2); obs = obs(:,randsample(numPred,ceil(numPred/3))); Mdl = fitcdiscr(obs,grp,'SaveMemory','on','FillCoeffs','off'); 

Cross validate the classifier.

Use 25 levels of Gamma and 25 levels of Delta to search for good parameters. This search is time consuming. Set Verbose to 1 to view the progress.

[err,gamma,delta,numpred] = cvshrink(Mdl,... 'NumGamma',24,'NumDelta',24,'Verbose',1); 
Done building cross-validated model. Processing Gamma step 1 out of 25. Processing Gamma step 2 out of 25. Processing Gamma step 3 out of 25. Processing Gamma step 4 out of 25. Processing Gamma step 5 out of 25. Processing Gamma step 6 out of 25. Processing Gamma step 7 out of 25. Processing Gamma step 8 out of 25. Processing Gamma step 9 out of 25. Processing Gamma step 10 out of 25. Processing Gamma step 11 out of 25. Processing Gamma step 12 out of 25. Processing Gamma step 13 out of 25. Processing Gamma step 14 out of 25. Processing Gamma step 15 out of 25. Processing Gamma step 16 out of 25. Processing Gamma step 17 out of 25. Processing Gamma step 18 out of 25. Processing Gamma step 19 out of 25. Processing Gamma step 20 out of 25. Processing Gamma step 21 out of 25. Processing Gamma step 22 out of 25. Processing Gamma step 23 out of 25. Processing Gamma step 24 out of 25. Processing Gamma step 25 out of 25. 

Examine the quality of the regularized classifiers.

Plot the number of predictors against the error.

figure; plot(err,numpred,'k.') xlabel('Error rate'); ylabel('Number of predictors'); 

Examine the lower-left part of the plot more closely.

axis([0 .1 0 1000]) 

There is a clear tradeoff between lower number of predictors and lower error.

Choose an optimal tradeoff between model size and accuracy.

Multiple pairs of Gamma and Delta values produce about the same minimal error. Display the indices of these pairs and their values.

minerr = min(min(err)) [p,q] = find(err < minerr + 1e-4); % Subscripts of err producing minimal error numel(p) idx = sub2ind(size(delta),p,q); % Convert from subscripts to linear indices [gamma(p) delta(idx)] 
minerr = 0.0139 ans = 4 ans = 0.7202 0.1145 0.7602 0.1131 0.8001 0.1128 0.8001 0.1410 

These points have as few as 20% of the total predictors that have nonzero coefficients in the model.

numpred(idx)/ceil(numPred/3)*100 
ans = 39.8051 38.9805 36.8066 28.7856 

To further lower the number of predictors, you must accept larger error rates. For example, to choose the Gamma and Delta that give the lowest error rate with 200 or fewer predictors.

low200 = min(min(err(numpred <= 200))); lownum = min(min(numpred(err == low200))); [low200 lownum] 
ans = 0.0185 173.0000 

You need 195 predictors to achieve an error rate of 0.0185, and this is the lowest error rate among those that have 200 predictors or fewer.

Display the Gamma and Delta that achieve this error/number of predictors.

[r,s] = find((err == low200) & (numpred == lownum)); [gamma(r); delta(r,s)] 
ans = 0.6403 0.2399 

Set the regularization parameters.

To set the classifier with these values of Gamma and Delta, use dot notation.

Mdl.Gamma = gamma(r); Mdl.Delta = delta(r,s); 

Heat map plot

To compare the cvshrink calculation to that in Guo, Hastie, and Tibshirani [3], plot heat maps of error and number of predictors against Gamma and the index of the Delta parameter. (The Delta parameter range depends on the value of the Gamma parameter. So to get a rectangular plot, use the Delta index, not the parameter itself.)

% Create the Delta index matrix indx = repmat(1:size(delta,2),size(delta,1),1); figure subplot(1,2,1) imagesc(err); colorbar; colormap('jet') title 'Classification error'; xlabel 'Delta index'; ylabel 'Gamma index'; subplot(1,2,2) imagesc(numpred); colorbar; title 'Number of predictors in the model'; xlabel 'Delta index' ; ylabel 'Gamma index' ; 

You see the best classification error when Delta is small, but fewest predictors when Delta is large.

### Examine the Gaussian Mixture Assumption

Discriminant analysis assumes that the data comes from a Gaussian mixture model (see Creating a Classifier Using fitcdiscr). If the data appears to come from a Gaussian mixture model, you can expect discriminant analysis to be a good classifier. Furthermore, the default linear discriminant analysis assumes that all class covariance matrices are equal. This section shows methods to check these assumptions:

#### Bartlett Test of Equal Covariance Matrices for Linear Discriminant Analysis

The Bartlett test (see Box [1]) checks equality of the covariance matrices of the various classes. If the covariance matrices are equal, the test indicates that linear discriminant analysis is appropriate. If not, consider using quadratic discriminant analysis, setting the DiscrimType name-value pair to 'quadratic' in fitcdiscr.

The Bartlett test assumes normal (Gaussian) samples, where neither the means nor covariance matrices are known. To determine whether the covariances are equal, compute the following quantities:

• Sample covariance matrices per class σi, 1 ≤ i ≤ k, where k is the number of classes.

• Pooled-in covariance matrix σ.

• Test statistic V:

$V=\left(n-k\right)\mathrm{log}\left(|\Sigma |\right)-\sum _{i=1}^{k}\left({n}_{i}-1\right)\mathrm{log}\left(|{\Sigma }_{i}|\right)$

where n is the total number of observations, and ni is the number of observations in class i, and |Σ| means the determinant of the matrix Σ.

• Asymptotically, as the number of observations in each class ni become large, V is distributed approximately χ2 with kd(d + 1)/2 degrees of freedom, where d is the number of predictors (number of dimensions in the data).

The Bartlett test is to check whether V exceeds a given percentile of the χ2 distribution with kd(d + 1)/2 degrees of freedom. If it does, then reject the hypothesis that the covariances are equal.

Example: Bartlett Test for Equal Covariance Matrices.  Check whether the Fisher iris data is well modeled by a single Gaussian covariance, or whether it would be better to model it as a Gaussian mixture.

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; L = fitcdiscr(meas,species,'PredictorNames',prednames); Q = fitcdiscr(meas,species,'PredictorNames',prednames,'DiscrimType','quadratic'); D = 4; % Number of dimensions of X Nclass = [50 50 50]; N = L.NumObservations; K = numel(L.ClassNames); SigmaQ = Q.Sigma; SigmaL = L.Sigma; logV = (N-K)*log(det(SigmaL)); for k=1:K logV = logV - (Nclass(k)-1)*log(det(SigmaQ(:,:,k))); end nu = (K-1)*D*(D+1)/2; pval = 1 - chi2cdf(logV,nu) 
pval = 0 

The Bartlett test emphatically rejects the hypothesis of equal covariance matrices. If pval had been greater than 0.05, the test would not have rejected the hypothesis. The result indicates to use quadratic discriminant analysis, as opposed to linear discriminant analysis.

#### Q-Q Plot

A Q-Q plot graphically shows whether an empirical distribution is close to a theoretical distribution. If the two are equal, the Q-Q plot lies on a 45° line. If not, the Q-Q plot strays from the 45° line.

Check Q-Q Plots for Linear and Quadratic Discriminants.  For linear discriminant analysis, use a single covariance matrix for all classes.

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; L = fitcdiscr(meas,species,'PredictorNames',prednames); N = L.NumObservations; K = numel(L.ClassNames); mahL = mahal(L,L.X,'ClassLabels',L.Y); D = 4; expQ = chi2inv(((1:N)-0.5)/N,D); % expected quantiles [mahL,sorted] = sort(mahL); % sorted obbserved quantiles figure; gscatter(expQ,mahL,L.Y(sorted),'bgr',[],[],'off'); legend('virginica','versicolor','setosa','Location','NW'); xlabel('Expected quantile'); ylabel('Observed quantile'); line([0 20],[0 20],'color','k'); 

Overall, the agreement between the expected and observed quantiles is good. Look at the right half of the plot. The deviation of the plot from the 45° line upward indicates that the data has tails heavier than a normal distribution. There are three possible outliers on the right: two observations from class 'setosa' and one observation from class 'virginica'.

As shown in Bartlett Test of Equal Covariance Matrices for Linear Discriminant Analysis, the data does not match a single covariance matrix. Redo the calculations for a quadratic discriminant.

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; Q = fitcdiscr(meas,species,'PredictorNames',prednames,'DiscrimType','quadratic'); Nclass = [50 50 50]; N = L.NumObservations; K = numel(L.ClassNames); mahQ = mahal(Q,Q.X,'ClassLabels',Q.Y); expQ = chi2inv(((1:N)-0.5)/N,D); [mahQ,sorted] = sort(mahQ); figure; gscatter(expQ,mahQ,Q.Y(sorted),'bgr',[],[],'off'); legend('virginica','versicolor','setosa','Location','NW'); xlabel('Expected quantile'); ylabel('Observed quantile for QDA'); line([0 20],[0 20],'color','k'); 

The Q-Q plot shows a better agreement between the observed and expected quantiles. There is only one outlier candidate, from class 'setosa'.

#### Mardia Kurtosis Test of Multivariate Normality

The Mardia kurtosis test (see Mardia [4]) is an alternative to examining a Q-Q plot. It gives a numeric approach to deciding if data matches a Gaussian mixture model.

In the Mardia kurtosis test you compute M, the mean of the fourth power of the Mahalanobis distance of the data from the class means. If the data is normally distributed with constant covariance matrix (and is thus suitable for linear discriminant analysis), M is asymptotically distributed as normal with mean d(d + 2) and variance 8d(d + 2)/n, where

• d is the number of predictors (number of dimensions in the data).

• n is the total number of observations.

The Mardia test is two sided: check whether M is close enough to d(d + 2) with respect to a normal distribution of variance 8d(d + 2)/n.

Example: Mardia Kurtosis Test for Linear and Quadratic Discriminants.  Check whether the Fisher iris data is approximately normally distributed for both linear and quadratic discriminant analysis. According to Bartlett Test of Equal Covariance Matrices for Linear Discriminant Analysis, the data is not normal for linear discriminant analysis (the covariance matrices are different). Check Q-Q Plots for Linear and Quadratic Discriminants indicates that the data is well modeled by a Gaussian mixture model with different covariances per class. Check these conclusions with the Mardia kurtosis test:

load fisheriris; prednames = {'SepalLength','SepalWidth','PetalLength','PetalWidth'}; L = fitcdiscr(meas,species,'PredictorNames',prednames); mahL = mahal(L,L.X,'ClassLabels',L.Y); D = 4; N = L.NumObservations; obsKurt = mean(mahL.^2); expKurt = D*(D+2); varKurt = 8*D*(D+2)/N; [~,pval] = ztest(obsKurt,expKurt,sqrt(varKurt)) 
pval = 0.0208 

The Mardia test indicates to reject the hypothesis that the data is normally distributed.

Continuing the example with quadratic discriminant analysis:

Q = fitcdiscr(meas,species,'PredictorNames',prednames,'DiscrimType','quadratic'); mahQ = mahal(Q,Q.X,'ClassLabels',Q.Y); obsKurt = mean(mahQ.^2); [~,pval] = ztest(obsKurt,expKurt,sqrt(varKurt)) 
pval = 0.7230 

Because pval is high, you conclude the data are consistent with the multivariate normal distribution.

### Bibliography

[1] Box, G. E. P. A General Distribution Theory for a Class of Likelihood Criteria. Biometrika 36(3), pp. 317–346, 1949.

[2] Fisher, R. A. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, Vol. 7, pp. 179–188, 1936. Available at http://digital.library.adelaide.edu.au/dspace/handle/2440/15227.

[3] Guo, Y., T. Hastie, and R. Tibshirani. Regularized Discriminant Analysis and Its Application in Microarray. Biostatistics, Vol. 8, No. 1, pp. 86–100, 2007.

[4] Mardia, K. V. Measures of multivariate skewness and kurtosis with applications. Biometrika 57 (3), pp. 519–530, 1970.