MATLAB 中 随机森林分类器、决策树分类器、KNN、贝叶斯分类器的分类函数是什么，分别如何进行二分类，给出实例代码

MATLAB 中随机森林分类器、决策树分类器、KNN、贝叶斯分类器的分类函数如下：

- 随机森林分类器：predict
- 决策树分类器：predict
- KNN：predict
- 贝叶斯分类器：classify

下面分别给出这四种分类器的二分类实例代码：

1. 随机森林分类器

% 加载数据集
load fisheriris
X = meas(:,3:4);
Y = species;

% 将标签转换为二分类问题
Y(strcmp(Y, 'setosa')) = 'positive';
Y(~strcmp(Y, 'positive')) = 'negative';

% 划分训练集和测试集
cv = cvpartition(Y, 'HoldOut', 0.3);
Xtrain = X(cv.training,:);
Ytrain = Y(cv.training);
Xtest = X(cv.test,:);
Ytest = Y(cv.test);

% 训练随机森林分类器
t = templateTree('Surrogate','on');
model = fitcensemble(Xtrain, Ytrain, 'Method', 'Bag', 'NumLearningCycles', 100, 'Learners', t);

% 预测测试集
Ypred = predict(model, Xtest);

% 计算分类准确率
accuracy = sum(strcmp(Ypred, Ytest)) / length(Ytest);

2. 决策树分类器

% 加载数据集
load fisheriris
X = meas(:,3:4);
Y = species;

% 将标签转换为二分类问题
Y(strcmp(Y, 'setosa')) = 'positive';
Y(~strcmp(Y, 'positive')) = 'negative';

% 划分训练集和测试集
cv = cvpartition(Y, 'HoldOut', 0.3);
Xtrain = X(cv.training,:);
Ytrain = Y(cv.training);
Xtest = X(cv.test,:);
Ytest = Y(cv.test);

% 训练决策树分类器
model = fitctree(Xtrain, Ytrain);

% 预测测试集
Ypred = predict(model, Xtest);

% 计算分类准确率
accuracy = sum(strcmp(Ypred, Ytest)) / length(Ytest);

3. KNN

% 加载数据集
load fisheriris
X = meas(:,3:4);
Y = species;

% 将标签转换为二分类问题
Y(strcmp(Y, 'setosa')) = 'positive';
Y(~strcmp(Y, 'positive')) = 'negative';

% 划分训练集和测试集
cv = cvpartition(Y, 'HoldOut', 0.3);
Xtrain = X(cv.training,:);
Ytrain = Y(cv.training);
Xtest = X(cv.test,:);
Ytest = Y(cv.test);

% 训练KNN分类器
model = fitcknn(Xtrain, Ytrain);

% 预测测试集
Ypred = predict(model, Xtest);

% 计算分类准确率
accuracy = sum(strcmp(Ypred, Ytest)) / length(Ytest);

4. 贝叶斯分类器

% 加载数据集
load fisheriris
X = meas(:,3:4);
Y = species;

% 将标签转换为二分类问题
Y(strcmp(Y, 'setosa')) = 'positive';
Y(~strcmp(Y, 'positive')) = 'negative';

% 划分训练集和测试集
cv = cvpartition(Y, 'HoldOut', 0.3);
Xtrain = X(cv.training,:);
Ytrain = Y(cv.training);
Xtest = X(cv.test,:);
Ytest = Y(cv.test);

% 训练贝叶斯分类器
model = fitcnb(Xtrain, Ytrain);

% 预测测试集
Ypred = predict(model, Xtest);

% 计算分类准确率
accuracy = sum(strcmp(Ypred, Ytest)) / length(Ytest);