0-总程序代码在这里

ww2.mathworks.cn/matlabcentr…

安装细节见下文

1-下载xgboost，配置Cmake

make folder D:\xgboost

create an empty git repository
pull from github.com/dmlc/xgboos…
Git bash here (D:\r\xgboost) - open a git bash. In it type:
git submodule init
git submodule update
install cmake and add path to the env (automatically, just select the option)

查看电脑中是否有cmake，cmd 中输入以下代码：

如果没有安装，则根据msi 下载链接安装：

= cgold.readthedocs.io/en/latest/f…

= download and install: github.com/Kitware/CMa…

2-build

In dos, go to folder D:\r\xgboost. In it execute:

mkdir build

cd build

cmake .. -G"Visual Studio 14 2015 Win64"

for VS15: cmake .. -G"Visual Studio 15 2017" -A x64

for VS16: cmake .. -G"Visual Studio 16 2019" -A x64

这边容易出现报错信息：

CMake Error at CMakeLists.txt:3 (project):
  Failed to run MSBuild command:

    MSBuild.exe

  to get the value of VCTargetsPath:
Configuring incomplete, errors occurred!

解决方案：

0. 安装“Visual Studio Installer”

docs.microsoft.com/en-us/visua…

上面网址可下载。

1.检查是否安装 “使用C++的桌面开发”

2.检查是否安装 “Visual Studio 扩展开发”

3.点开“单个组件”检查是否安装了以下组件

4.检查计算机变量，是否有以下这个环境变量。

cmake --build . --config Release

Result:

xgboost.dll is created here: "D:\r\xgboost\lib\xgboost.dll"

3-配置头文件

download header file: raw.githubusercontent.com/dmlc/xgboos…
save it to "D:\r\xgboost\lib"
rename c_api.h to xgboost.h

Result:

xgboost.h is created here: "D:\r\xgboost\lib\xgboost.h"

4-下载wheel 文件，修改 xgboost_install.m 并运行xgboost_train.m

下载wheel 文件

s3-us-west-2.amazonaws.com/xgboost-nig…

上面的网址可以下载

修改xgboost_install.m 中安装文件的位置

In xgboost_test.m and xgboost_train.m, change D:\r\xgboost\lib to the location of xgboost (only if you put it in another directory)

这几步OK后运行xgboost_install.m xgboost 安装完毕

5-运行测试用例

clc;
clear;
load carsmall; 
Xtrain = [Acceleration Cylinders Displacement Horsepower MPG]; 
ytrain = cellstr(Origin); 
ytrain = double(ismember(ytrain,'USA'));
X = Xtrain(1:70,:);
y = ytrain(1:70); 
Xtest = Xtrain(size(X,1)+1:end,:);
ytest = ytrain(size(X,1)+1:end);
model_filename = 'xgboost_model.xgb'; 
model = xgboost_train(X,y,[],999,'AUC',model_filename); %%% model_filename = 'xgboost_model.xgb'
loadmodel = 0; 
Yhat = xgboost_test(Xtest,model,loadmodel);
[XX,YY,~,AUC] = perfcurve(ytest,Yhat,1);
figure; 
plot(XX,YY,'LineWidth',2); xlabel('False positive rate'); ylabel('True positive rate'); title('ROC for Classification by Logistic Regression'); grid on
figure; 
scatter(Yhat,ytest + 0.1*rand(length(ytest),1)); grid on
unloadlibrary('xgboost')

[xgboost] matlab 版本_windows 安装指南