Regression prediction | MATLAB implements PSO-RF particle swarm optimization algorithm to optimize random forest algorithm multi-input single-output regression prediction (multi-indicator, multi-graph)

Regression prediction | MATLAB implements PSO-RF particle swarm optimization algorithm to optimize random forest algorithm multi-input single-output regression prediction (multi-indicator, multi-graph)

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basic introduction

Regression prediction | MATLAB implements PSO-RF particle swarm optimization algorithm to optimize random forest algorithm multiple input single output regression prediction (multiple indicators, multiple graphs), input multiple features, output single variable, multiple input single output regression prediction; multi-index evaluation
, The code quality is extremely high; excel data is easy to replace, and the operating environment is 2018 and above.

programming

• Complete source code and data acquisition method: private message reply PSO-RF particle swarm optimization algorithm optimization random forest algorithm multi-input single-output regression prediction (multi-indicator, multi-graph) .

%%  清空环境变量
warning off             % 关闭报警信息
close all               % 关闭开启的图窗
clear                   % 清空变量
clc                     % 清空命令行

%%  导入数据
res = xlsread('data.xlsx');

%%  划分训练集和测试集
temp = randperm(103);

P_train = res(temp(1: 80), 1: 7)';
T_train = res(temp(1: 80), 8)';
M = size(P_train, 2);

P_test = res(temp(81: end), 1: 7)';
T_test = res(temp(81: end), 8)';
N = size(P_test, 2);

%%  数据归一化
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input);

[t_train, ps_output] = mapminmax(T_train, 0, 1);
t_test = mapminmax('apply', T_test, ps_output);



%%  仿真测试
t_sim1 = sim(net, p_train);
t_sim2 = sim(net, p_test);

%%  数据反归一化
T_sim1 = mapminmax('reverse', t_sim1, ps_output);
T_sim2 = mapminmax('reverse', t_sim2, ps_output);

%%  均方根误差
error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M);
error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N);



%%  相关指标计算
% 决定系数 R2
R1 = 1 - norm(T_train - T_sim1)^2 / norm(T_train - mean(T_train))^2;
R2 = 1 - norm(T_test -  T_sim2)^2 / norm(T_test -  mean(T_test ))^2;

disp(['训练集数据的R2为：', num2str(R1)])
disp(['测试集数据的R2为：', num2str(R2)])

% 平均绝对误差 MAE
mae1 = sum(abs(T_sim1 - T_train)) ./ M ;
mae2 = sum(abs(T_sim2 - T_test )) ./ N ;

disp(['训练集数据的MAE为：', num2str(mae1)])
disp(['测试集数据的MAE为：', num2str(mae2)])

% 平均相对误差 MBE
mbe1 = sum(T_sim1 - T_train) ./ M ;
mbe2 = sum(T_sim2 - T_test ) ./ N ;

disp(['训练集数据的MBE为：', num2str(mbe1)])
disp(['测试集数据的MBE为：', num2str(mbe2)])

References

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718