Discriminant analysis is a classification method. It assumes that different classes generate data based on different Gaussian distributions.
To train (create) a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class.
To predict the classes of new data, the trained classifier finds the class with the smallest misclassification cost .
Linear discriminant analysis is also known as the Fisher discriminant, named for its inventor, Sir R. A. Fisher [1].
This example shows how to train a basic discriminant analysis classifier to classify irises in Fisher's iris data.
Load the 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.
Classify an iris with average measurements.
meanmeas = mean(meas); meanclass = predict(MdlLinear,meanmeas)
meanclass = 1x1 cell array
{'versicolor'}
Create a quadratic classifier.
MdlQuadratic = fitcdiscr(meas,species,'DiscrimType','quadratic');
To visualize the classification boundaries of a 2-D quadratic classification of the data.
Classify an iris with average measurements using the quadratic classifier.
meanclass2 = predict(MdlQuadratic,meanmeas)
meanclass2 = 1x1 cell array
{'versicolor'}