{"id":3573,"date":"2023-07-16T18:41:15","date_gmt":"2023-07-16T18:41:15","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%bb%9f%e8%ae%a1%e6%a8%a1%e5%9e%8b%e9%a2%84%e6%b5%8b\/"},"modified":"2023-07-16T18:41:15","modified_gmt":"2023-07-16T18:41:15","slug":"%e7%bb%9f%e8%ae%a1%e6%a8%a1%e5%9e%8b%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%bb%9f%e8%ae%a1%e6%a8%a1%e5%9e%8b%e9%a2%84%e6%b5%8b\/","title":{"rendered":"\u5982\u4f55\u4f7f\u7528 statsmodels \u4e2d\u7684\u56de\u5f52\u6a21\u578b\u8fdb\u884c\u9884\u6d4b"},"content":{"rendered":"

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\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u6765\u4f7f\u7528 Python \u4e2d\u7684statsmodels<\/a>\u6a21\u5757\u8fdb\u884c\u56de\u5f52\u6a21\u578b\u62df\u5408\uff0c\u4ee5\u5bf9\u65b0\u89c2\u6d4b\u503c\u8fdb\u884c\u9884\u6d4b\uff1a<\/span><\/p>\n

 model. predict<\/span> (df_new)\n<\/strong><\/pre>\n
\u8fd9\u79cd\u7279\u6b8a\u7684\u8bed\u6cd5\u5c06\u4f7f\u7528\u9002\u5408\u7edf\u8ba1\u6a21\u578b\u7684\u56de\u5f52\u6a21\u578b\uff08\u79f0\u4e3amodel \uff09<\/strong>\u8ba1\u7b97\u540d\u4e3adf_new<\/strong>\u7684\u65b0 DataFrame \u7684\u6bcf\u4e00\u884c\u7684\u9884\u6d4b\u54cd\u5e94\u503c\u3002<\/span><\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6b64\u8bed\u6cd5\u3002<\/span><\/p>\n
\u793a\u4f8b\uff1a\u4f7f\u7528 Statsmodels \u4e2d\u7684\u56de\u5f52\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u67d0\u4e2a\u73ed\u7ea7\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u95f4\u3001\u51c6\u5907\u8003\u8bd5\u4ee5\u53ca\u6700\u7ec8\u6210\u7ee9\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6],\n                   ' exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2],\n                   ' score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96]})\n\n#view head of DataFrame\n<\/span>df. head<\/span> ()\n\n\thours exam score\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72<\/strong><\/pre>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define predictor and response variables\n<\/span>y = df[' score<\/span> ']\nx = df[[' hours<\/span> ', ' exams<\/span> ']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.718\nModel: OLS Adj. R-squared: 0.661\nMethod: Least Squares F-statistic: 12.70\nDate: Fri, 05 Aug 2022 Prob (F-statistic): 0.00180\nTime: 09:24:38 Log-Likelihood: -38.618\nNo. Observations: 13 AIC: 83.24\nDf Residuals: 10 BIC: 84.93\nDf Model: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.4048 4.001 17.847 0.000 62.490 80.319\nhours 5.1275 1.018 5.038 0.001 2.860 7.395\nexams -1.2121 1.147 -1.057 0.315 -3.768 1.344\n==================================================== ============================\nOmnibus: 1,103 Durbin-Watson: 1,248\nProb(Omnibus): 0.576 Jarque-Bera (JB): 0.803\nSkew: -0.289 Prob(JB): 0.669\nKurtosis: 1.928 Cond. No. 11.7\n==================================================== ============================\n<\/strong><\/pre>\n
\u4ece\u8f93\u51fa\u4e2d\u7684coef<\/strong>\u5217\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u62df\u5408\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
\u5206\u6570 = 71.4048 + 5.1275\uff08\u5c0f\u65f6\uff09\u2013 1.2121\uff08\u8003\u8bd5\uff09<\/span><\/p>\n
\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u8981\u4f7f\u7528\u62df\u5408\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u4e94\u540d\u65b0\u751f\u7684\u201c\u5206\u6570\u201d\u3002<\/span><\/p>\n
\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u521b\u5efa\u4e00\u4e2a DataFrame \u6765\u4fdd\u5b58\u4e94\u4e2a\u65b0\u7684\u89c2\u5bdf\u7ed3\u679c\uff1a<\/span><\/p>\n
 #create new DataFrame\ndf_new = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 5],\n                       ' exams<\/span> ': [1, 1, 4, 3, 3]})<\/span>\n\n#add column for constant\ndf_new = sm. add_constant<\/span> (df_new)\n<\/span>\n#view new DataFrame\nprint<\/span> (df_new)\n\n   const hours exams\n0 1.0 1 1\n1 1.0 2 1\n2 1.0 2 4\n3 1.0 4 3\n4 1.0 5 3<\/span><\/span><\/strong><\/pre>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Predict()<\/strong>\u51fd\u6570\u6765\u9884\u6d4b\u6bcf\u4e2a\u5b66\u751f\u7684\u201c\u5206\u6570\u201d\uff0c\u4f7f\u7528\u201c\u5c0f\u65f6\u201d\u548c\u201c\u8003\u8bd5\u201d\u4f5c\u4e3a\u62df\u5408\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u9884\u6d4b\u53d8\u91cf\u7684\u503c\uff1a<\/span><\/span><\/p>\n
 #predict scores for the five new students<\/span>\nmodel. predict<\/span> (df_new)\n\n0 75.320242\n1 80.447734\n2 76.811480\n3 88.278550\n4 93.406042\ndtype:float64\n<\/strong><\/pre>\n
\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\uff1a<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 statsmodels \u6a21\u5757\u7684OLS()<\/strong>\u51fd\u6570\u6765\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\uff0c\u4f7f\u7528\u201c\u5c0f\u65f6\u201d\u548c\u201c\u8003\u8bd5\u201d\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c\u201c\u5206\u6570\u201d\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff1a<\/span><\/p>\n