Multi-Class Classification using SVM : One vs. All | MATLAB Implementation

Code: clc clear all close all warning off load fisheriris X=meas(:,3:4); Y=species; figure gscatter(X(:,1),X(:,2),Y); xlabel('Petal Length (cm)'); ylabel('Petal Width (cm)'); classes=unique(Y); ms=length(classes); SVMModels=cell(ms,1); for j = 1:numel(classes) indx=strcmp(Y,classes(j)); % Create binary classes for each classifier SVMModels{j}=fitcsvm(X,indx,'ClassNames',[false true],'Standardize',true,... 'KernelFunction','polynomial'); end e=min(X(:,1)):0.01:max(X(:,1)); f=min(X(:,2)):0.01:max(X(:,2)); [x1 x2]=meshgrid(e,f); x=[x1(:) x2(:)]; N=size(x,1); Scores=zeros(N,numel(classes)); for j=1:numel(classes) [~,score]=predict(SVMModels{j},x); Scores(:,j)=score(:,2); % Second column contains positive-class scores end [~,maxScore]=max(Scores,[],2); figure gscatter(x1(:),x2(:),maxScore,'cym'); hold on; gscatter(X(:,1),X(:,2),Y,'rgb','.',30); title('{\bf Iris Classification Regions}'); xlabel('Petal Length (cm)'); ylabel('Petal Width (cm)'); axis tight hold off Learn Complete Machine Learning & Data Science using MATLAB: https://www.youtube.com/playlist?list=PLjfRmoYoxpNoaZmR2OTVrh-72YzLZBlJ2 Learn Digital Signal Processing using MATLAB: https://www.youtube.com/playlist?list=PLjfRmoYoxpNr3w6baU91ZM6QL0obULPig Learn Complete Image Processing & Computer Vision using MATLAB: https://www.youtube.com/playlist?list=PLjfRmoYoxpNostbIaNSpzJr06mDb6qAJ0 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 THINGS to support my channel LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL #DataScience #MachineLearning #MATLAB