MATLAB 支持向量回归：竞价与曝光次数关系分析

%20%E5%AF%BC%E5%85%A5%E6%95%B0%E6%8D%AE%0Adata%20%3D%20xlsread('Impression%26%26CPC.xlsx',%20'Sheet1');%0Abid%20%3D%20data(:,%202);%20%25%20%E7%AB%9E%E4%BB%A3%0Aimpression%20%3D%20data(:,%201);%20%25%20%E6%9B%9D%E5%85%A5%E6%AC%A1%E6%95%B0%0A%0A%25%20%E8%AE%A1%E7%AE%97%E6%9B%9D%E5%85%A5%E6%AC%A1%E6%95%B0%E7%9A%84%E4%B8%8A%E4%B8%8B%E5%9B%9B%E5%88%86%E4%BD%8D%E6%95%B0%E5%92%8CIQR%0AQ1%20%3D%20prctile(impression,%2025);%0AQ3%20%3D%20prctile(impression,%2075);%0AIQR%20%3D%20Q3%20-%20Q1;%0A%0A%25%20%E5%AE%9A%E4%B9%89%E5%BC%82%E5%B8%B8%E5%80%BC%E7%9A%84%E9%97%A8%E5%80%BC%EF%BC%88%E4%BA%A6%E5%A6%82%EF%BC%8C%E8%B6%85%E8%BF%872%E5%80%ABIQR%EF%BC%89%0Athreshold%20%3D%202%20*%20IQR;%0A%0A%25%20%E6%89%BE%E5%88%B0%E6%89%80%E6%9C%89%E5%90%AB%E5%BC%82%E5%B8%B8%E5%80%BC%E7%9A%84%E8%A1%8C%E7%B4%A2%E7%A4%BAE%0Aoutlier_rows%20%3D%20impression%20%3C%20(Q1%20-%20threshold)%20%7C%20impression%20%3E%20(Q3%20+%20threshold);%0A%0A%25%20%E5%88%A0%E9%99%A4%E5%90%AB%E5%BC%82%E5%B8%B8%E5%80%BC%E7%9A%84%E8%A1%8C%0Abid_cleaned%20%3D%20bid(~outlier_rows);%0Aimpression_cleaned%20%3D%20impression(~outlier_rows);%0A%0A%25%20%E6%94%AF%E6%8C%81%E7%90%86%E5%90%91%E5%9B%9E%E5%90%88%E6%8B%9F%E6%9F%A5%0Aepsilon%20%3D%200.1;%20%25%20%CE%B5%E5%8F%82%E6%95%B0%EF%BC%8C%E6%8E%A7%E5%88%B6%E6%8B%9F%E6%9F%A5%E7%9A%84%E5%AE%B9%E8%A8%8A%E5%BA%A6%0Amodel%20%3D%20fitrlinear(bid_cleaned,%20impression_cleaned,%20'Epsilon',%20epsilon);%0A%0A%25%20%E9%A8%8E%E6%B8%AC%E6%8B%9F%E6%9F%A5%E5%80%BC%0Ay_fit%20%3D%20predict(model,%20bid_cleaned);%0A%0A%25%20%E6%95%B0%E6%8D%AE%E5%8F%AF%E8%A7%86%E5%8C%96%EF%BC%88%E5%88%A0%E9%99%A4%E5%BC%82%E5%B8%B8%E5%80%BC%E5%90%8E%EF%BC%89%0Ascatter(bid_cleaned,%20impression_cleaned);%0Ahold%20on;%0Aplot(bid_cleaned,%20y_fit,%20'r');%0Axlabel('竞价');%0Aylabel('曝光次数');%0Atitle('竞价与曝光次数关系（支持向量回归拟合）');%0Alegend('数据点',%20'拟合线');%0Ahold%20off;%0A%0A%25%20%E8%AE%A1%E7%AE%97%E6%8B%9F%E6%9F%A5%E6%9B%B2%E7%BA%BF%E4%B8%8E%E5%AE%9E%E9%99%85%E6%95%B0%E6%8D%AE%E4%B9%8B%E9%97%B4%E7%9A%84%E7%9B%B8%E5%85%B3%E7%B3%BB%E6%95%B0%0Acorrelation%20%3D%20corrcoef(impression_cleaned,%20y_fit);%0Ar%20%3D%20correlation(1,%202);%0A%0A%25%20%E8%BE%93%E5%87%BA%E7%9B%B8%E5%85%B3%E7%B3%BB%E6%95%B0%0Adisp(['相关系数：',%20num2str(r)]);%0A%E5%A2%9E%E5%A4%A7%E7%9B%B8%E5%85%B3%E7%B3%BB%E6%95%B0%E5%88%B00.9%0A%E6%94%B9%E5%86%99%E4%BB%A3%E7%A0%81%0A%3F%3F%3F%20Undefined%20function%20or%20method%20'fitrsvm'%20for%20input%20arguments%20of%20type%20'double'.%E5%86%8D%E7%A0%81%E5%87%BA%E5%90%8D%E5%8F%98%E6%9B%B4%E5%9C%A8MATLAB%E4%B8%AD%EF%BC%8C%E6%B2%A1%E6%9C%89fitrsvm%E5%87%BD%E6%95%B0%EF%BC%8C%E5%8F%AF%E8%83%BD%E6%98%AF%E5%9B%A0%E4%B8%BA%E6%82%A8%E4%BD%BF%E7%94%A8%E7%9A%84%E6%98%AF%E8%BF%87%E5%8E%BB%E7%89%88%E6%9C%AC%E3%80%82%0A%0A%E5%9C%A8%E6%9C%80%E6%96%B0%E7%89%88%E6%9C%AC%E4%B8%AD%EF%BC%8C%E5%8F%AF%E4%BB%A5%E4%BD%BF%E7%94%A8fitrlinear%E5%87%BD%E6%95%B0%E6%9D%A5%E8%BF%9B%E8%A1%8C%E6%94%AF%E6%8C%81%E7%90%86%E5%90%91%E5%9B%9E%E5%90%88%E3%80%82%E4%BB%A5%E4%B8%8B%E6%98%AF%E6%94%B9%E5%86%99%E5%90%8E%E7%9A%84%E4%BB%A3%E7%A0%81%EF%3A%0A%0A```matlab% 导入数据data = xlsread('Impression&&CPC.xlsx', 'Sheet1');bid = data(:, 2); % 竞价impression = data(:, 1); % 曝光次数

% 计算曝光次数的上下四分位数和IQRQ1 = prctile(impression, 25);Q3 = prctile(impression, 75);IQR = Q3 - Q1;

% 定义异常值的阈值（例如，超过2倍IQR）threshold = 2 * IQR;

% 找到所有包含异常值的行索引outlier_rows = impression < (Q1 - threshold) | impression > (Q3 + threshold);

% 删除包含异常值的行bid_cleaned = bid(~outlier_rows);impression_cleaned = impression(~outlier_rows);

% 支持向量回归拟合epsilon = 0.1; % ε参数，控制拟合的容忍度model = fitrlinear(bid_cleaned, impression_cleaned, 'Epsilon', epsilon);

% 预测拟合值y_fit = predict(model, bid_cleaned);

% 数据可视化（删除异常值后）scatter(bid_cleaned, impression_cleaned);hold on;plot(bid_cleaned, y_fit, 'r');xlabel('竞价');ylabel('曝光次数');title('竞价与曝光次数关系（支持向量回归拟合）');legend('数据点', '拟合线');hold off;

% 计算拟合曲线与实际数据之间的相关系数correlation = corrcoef(impression_cleaned, y_fit);r = correlation(1, 2);

% 输出相关系数disp(['相关系数：', num2str(r)]);

请注意，这段代码假设您的Excel文件中的第一个工作表中有两列数据，第一列是曝光次数，第二列是竞价。如果您的数据结构与此不同，请相应地修改代码中的数据导入部分。

另外，注意在计算相关系数时，我们使用了删除异常值后的数据。如果您希望计算包含异常值的数据的相关系数，可以将impression_cleaned和y_fit替换为impression和y_fit。

希望这对您有所帮助！如有其他问题，请随时提