import pandas as pd import numpy as np from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt from sklearn.model_selection import train_test

您想将以上的Python代码转化为MATLAB代码，是吗？以下是等效的MATLAB代码：

% 读取数据
data = readtable('附件0.2.xlsx');
 
% 分割自变量和目标变量
X = data{:, 2:end};
y = data{:, 1};

% 划分训练集和测试集
[X_train, X_test, y_train, y_test] = trainTestSplit(X, y, 'testSize', 0.3);
 
% 训练模型
n_estimators = 100;
rf = TreeBagger(n_estimators, X_train, y_train);

% 预测结果
y_pred = predict(rf, X_test);

% 转换预测结果格式为double类型
y_pred = str2double(y_pred);

% 计算MSE和R-squared
mse = mean((y_test - y_pred).^2);
r2 = 1 - sum((y_test - y_pred).^2) / sum((y_test - mean(y_test)).^2);

% 输出模型评估结果和目标方程
disp(['MSE: ', num2str(mse)]);
disp(['R-squared: ', num2str(r2)]);

% 输出目标方程系数
feature_importance = rf.OOBPermutedPredictorDeltaError';
feature_names = data.Properties.VariableNames(2:end)';
[~, sorted_idx] = sort(feature_importance);

fprintf('目标方程：\n');
for i = sorted_idx'
    fprintf('%f * %s +\n', feature_importance(i), char(feature_names(i)));
end

% 绘制特征重要性条形图
figure;
barh(feature_importance(sorted_idx));
set(gca, 'YTick', 1:length(feature_importance), 'YTickLabel', feature_names(sorted_idx), 'FontSize', 5);
xlabel('特征重要性');
ylabel('特征名称');
title('随机森林回归特征重要性');
saveas(gcf, '随机森林回归特征重要性.png');

希望这能满足您的需求！请确保在运行MATLAB代码之前已经安装了相应的工具箱（如Statistics and Machine Learning Toolbox）并将数据文件 附件0.2.xlsx 放置在当前 MATLAB 工作目录中。

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