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

Load the sample 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])

`ans = `*2x1 cell*
{'versicolor'}
{'virginica' }

K = MdlLinear.Coeffs(2,3).Const; L = MdlLinear.Coeffs(2,3).Linear;

Plot the curve that separates the second and third classes

$$K+\left[\begin{array}{cc}{x}_{1}& {x}_{2}\end{array}\right]L=0.$$

f = @(x1,x2) K + L(1)*x1 + L(2)*x2; h2 = fimplicit(f,[.9 7.1 0 2.5]); h2.Color = 'r'; h2.LineWidth = 2; h2.DisplayName = 'Boundary between Versicolor & Virginica';

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

MdlLinear.ClassNames([1 2])

`ans = `*2x1 cell*
{'setosa' }
{'versicolor'}

K = MdlLinear.Coeffs(1,2).Const; L = MdlLinear.Coeffs(1,2).Linear;

Plot the curve that separates the first and second classes.

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

Create a quadratic discriminant classifier.

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])

`ans = `*2x1 cell*
{'versicolor'}
{'virginica' }

K = MdlQuadratic.Coeffs(2,3).Const; L = MdlQuadratic.Coeffs(2,3).Linear; Q = MdlQuadratic.Coeffs(2,3).Quadratic;

Plot the curve that separates the second and third classes

$$K+\left[\begin{array}{cc}{x}_{1}& {x}_{2}\end{array}\right]L+\left[\begin{array}{cc}{x}_{1}& {x}_{2}\end{array}\right]Q\left[\begin{array}{c}{x}_{1}\\ {x}_{2}\end{array}\right]=0.$$

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 = fimplicit(f,[.9 7.1 0 2.5]); h2.Color = 'r'; h2.LineWidth = 2; h2.DisplayName = 'Boundary between Versicolor & Virginica';

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

MdlQuadratic.ClassNames([1 2])

`ans = `*2x1 cell*
{'setosa' }
{'versicolor'}

K = MdlQuadratic.Coeffs(1,2).Const; L = MdlQuadratic.Coeffs(1,2).Linear; Q = MdlQuadratic.Coeffs(1,2).Quadratic;

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 = fimplicit(f,[.9 7.1 0 1.02]); % Plot the relevant portion of the curve. h3.Color = 'k'; h3.LineWidth = 2; h3.DisplayName = 'Boundary between Versicolor & Setosa'; axis([.9 7.1 0 2.5]) xlabel('Petal Length') ylabel('Petal Width') title('{\bf Quadratic Classification with Fisher Training Data}') hold off