稀疏贝叶斯回归预测 matlab举例

以下是使用Matlab进行稀疏贝叶斯回归预测的示例代码：

加载数据：

load diabetes
X = diabetes(:,1:8);
y = diabetes(:,9);

拆分数据集：

cv = cvpartition(size(X,1),'HoldOut',0.3);
idx = cv.test;

Xtrain = X(~idx,:);
ytrain = y(~idx,:);
Xtest  = X(idx,:);
ytest  = y(idx,:);

训练模型：

% 安装稀疏贝叶斯包
% 在Matlab命令行中输入：addpath(genpath('sparsedata'))

% 设置参数
options = SB2_UserOptions('iterations',1000,'diagnosticLevel',0);

% 训练模型
model = SB2_ParameterPrior(Xtrain,ytrain,'gaussian',options);

预测：

ypred = SB2_Predict(Xtest,model);

计算误差：

mse = mean((ytest - ypred).^2)

完整代码：

load diabetes
X = diabetes(:,1:8);
y = diabetes(:,9);

cv = cvpartition(size(X,1),'HoldOut',0.3);
idx = cv.test;

Xtrain = X(~idx,:);
ytrain = y(~idx,:);
Xtest  = X(idx,:);
ytest  = y(idx,:);

options = SB2_UserOptions('iterations',1000,'diagnosticLevel',0);

model = SB2_ParameterPrior(Xtrain,ytrain,'gaussian',options);

ypred = SB2_Predict(Xtest,model);

mse = mean((ytest - ypred).^2)