iris數據集的中文名是安德森鳶尾花卉數據集，英文全稱是Anderson’s Iris data set。iris包含150個樣本，對應數據集的每行數據。每行數據包含每個樣本的四個特征和樣本的類別信息，所以iris數據集是一個150行5列的二維表。通俗地說，iris數據集是用來給花做分類的數據集，每個樣本包含了花萼長度、花萼寬度、花瓣長度、花瓣寬度四個特征（前4列），我們需要建立一個分類器，分類器可以通過樣本的四個特征來判斷樣本屬於山鳶尾、變色鳶尾還是維吉尼亞鳶尾（這三個名詞都是花的品種）。
數據的獲取：
file=importdata('iris.csv');%讀取csv文件中從第R-1行，第C-1列的數據開始的數據 data=file.data; features=data(:,1:4);%特征列表 classlabel=data(:,5);%對應類別 n = randperm(size(features,1));%隨機產生訓練集和測試集
繪制散點圖查看數據：
%% 繪制散點圖 class_0 = find(data(:,5)==0); class_1 = find(data(:,5)==1); class_2 = find(data(:,5)==2);%返回類別為2的位置索引 subplot(3,2,1) hold on scatter(features(class_0,1),features(class_0,2),'x','b') scatter(features(class_1,1),features(class_1,2),'+','g') scatter(features(class_2,1),features(class_2,2),'o','r') subplot(3,2,2) hold on scatter(features(class_0,1),features(class_0,3),'x','b') scatter(features(class_1,1),features(class_1,3),'+','g') scatter(features(class_2,1),features(class_2,3),'o','r') subplot(3,2,3) hold on scatter(features(class_0,1),features(class_0,4),'x','b') scatter(features(class_1,1),features(class_1,4),'+','g') scatter(features(class_2,1),features(class_2,4),'o','r') subplot(3,2,4) hold on scatter(features(class_0,2),features(class_0,3),'x','b') scatter(features(class_1,2),features(class_1,3),'+','g') scatter(features(class_2,2),features(class_2,3),'o','r') subplot(3,2,5) hold on scatter(features(class_0,2),features(class_0,4),'x','b') scatter(features(class_1,2),features(class_1,4),'+','g') scatter(features(class_2,2),features(class_2,4),'o','r') subplot(3,2,6) hold on scatter(features(class_0,3),features(class_0,4),'x','b') scatter(features(class_1,3),features(class_1,4),'+','g') scatter(features(class_2,3),features(class_2,4),'o','r')
曲線為根據花萼長度、花萼寬度、花瓣長度、花瓣寬度之間的關系繪制的散點圖。
訓練集與測試集：
%% 訓練集--70個樣本 train_features=features(n(1:70),:); train_label=classlabel(n(1:70),:); %% 測試集--30個樣本 test_features=features(n(71:end),:); test_label=classlabel(n(71:end),:);
數據歸一化：
%% 數據歸一化 [Train_features,PS] = mapminmax(train_features'); Train_features = Train_features'; Test_features = mapminmax('apply',test_features',PS); Test_features = Test_features';
使用SVM進行分類：
%% 創建/訓練SVM模型 model = svmtrain(train_label,Train_features); %% SVM仿真測試 [predict_train_label] = svmpredict(train_label,Train_features,model); [predict_test_label] = svmpredict(test_label,Test_features,model); %% 打印准確率 compare_train = (train_label == predict_train_label); accuracy_train = sum(compare_train)/size(train_label,1)*100; fprintf('訓練集准確率：%f\n',accuracy_train) compare_test = (test_label == predict_test_label); accuracy_test = sum(compare_test)/size(test_label,1)*100; fprintf('測試集准確率：%f\n',accuracy_test)
結果：
*
optimization finished, #iter = 18
nu = 0.668633
obj = -21.678546, rho = 0.380620
nSV = 30, nBSV = 28
*
optimization finished, #iter = 29
nu = 0.145900
obj = -3.676315, rho = -0.010665
nSV = 9, nBSV = 4
*
optimization finished, #iter = 21
nu = 0.088102
obj = -2.256080, rho = -0.133432
nSV = 7, nBSV = 2
Total nSV = 40
Accuracy = 97.1429% (68/70) (classification)
Accuracy = 97.5% (78/80) (classification)
訓練集准確率：97.142857
測試集准確率：97.500000