{"id":3011,"date":"2023-07-19T15:56:18","date_gmt":"2023-07-19T15:56:18","guid":{"rendered":"https:\/\/statorials.org\/cn\/sklearn%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92%e6%80%bb%e7%bb%93\/"},"modified":"2023-07-19T15:56:18","modified_gmt":"2023-07-19T15:56:18","slug":"sklearn%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92%e6%80%bb%e7%bb%93","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/sklearn%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92%e6%80%bb%e7%bb%93\/","title":{"rendered":"\u5982\u4f55\u4ece scikit-learn \u83b7\u53d6\u56de\u5f52\u6a21\u578b\u6458\u8981"},"content":{"rendered":"

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\u901a\u5e38\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u63d0\u53d6\u5728 Python \u4e2d\u4f7f\u7528scikit-learn<\/a>\u521b\u5efa\u7684\u56de\u5f52\u6a21\u578b\u7684\u6458\u8981\u3002<\/span><\/p>\n

\u4e0d\u5e78\u7684\u662f\uff0cscikit-learn \u6ca1\u6709\u63d0\u4f9b\u8bb8\u591a\u7528\u4e8e\u5206\u6790\u56de\u5f52\u6a21\u578b\u6458\u8981\u7684\u5185\u7f6e\u51fd\u6570\uff0c\u56e0\u4e3a\u5b83\u901a\u5e38\u4ec5\u7528\u4e8e\u9884\u6d4b\u76ee\u7684<\/a>\u3002<\/span><\/p>\n

\u56e0\u6b64\uff0c\u5982\u679c\u60a8\u60f3\u83b7\u5f97 Python \u56de\u5f52\u6a21\u578b\u7684\u6458\u8981\uff0c\u60a8\u6709\u4e24\u4e2a\u9009\u62e9\uff1a<\/span><\/p>\n

1.<\/strong>\u4f7f\u7528scikit-learn\u7684\u6709\u9650\u529f\u80fd\u3002<\/span><\/p>\n

2.<\/strong>\u6539\u7528\u7edf\u8ba1\u6a21\u578b<\/a>\u3002<\/span><\/p>\n

\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u4ee5\u4e0b pandas DataFrame \u7684\u6bcf\u79cd\u65b9\u6cd5\uff1a<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' x1<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4],\n                   ' x2<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4],\n                   ' y<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90]})\n\n#view first five rows of DataFrame\n<\/span>df. head<\/span> ()\n\n       x1 x2 y\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72\n<\/span><\/span><\/strong><\/pre>\n
\u65b9\u6cd5 1\uff1a\u4ece Scikit-Learn \u83b7\u53d6\u56de\u5f52\u6a21\u578b\u6458\u8981<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u4f7f\u7528 scikit-learn \u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a>\u6a21\u578b\uff1a<\/span><\/p>\n
 from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df[[' x1<\/span> ', ' x2<\/span> ']], df. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n<\/span><\/span><\/strong><\/pre>\n
 #display regression coefficients and R-squared value of model<\/span>\nprint<\/span> (model. intercept_<\/span> , model. coef_<\/span> , model. score<\/span> (X, y))\n\n70.4828205704 [5.7945 -1.1576] 0.766742556527\n<\/span><\/span><\/strong><\/pre>\n
\u4f7f\u7528\u6b64\u8f93\u51fa\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u62df\u5408\u56de\u5f52\u6a21\u578b\u7684\u65b9\u7a0b\uff1a<\/span><\/p>\n
 y = 70.48 + 5.79x 1<\/sub> \u2013 1.16x 2<\/sub><\/span><\/p>\n
\u8fd8\u53ef\u4ee5\u770b\u51fa\uff0c\u6a21\u578b\u7684R 2<\/sup>\u503c\u4e3a76.67\u3002<\/span><\/p>\n
\u8fd9\u610f\u5473\u7740\u54cd\u5e94\u53d8\u91cf\u4e2d76.67%<\/strong>\u7684\u53d8\u5f02\u53ef\u4ee5\u7531\u6a21\u578b\u4e2d\u7684\u4e24\u4e2a\u9884\u6d4b\u53d8\u91cf\u6765\u89e3\u91ca\u3002<\/span><\/p>\n
\u65b9\u6cd52\uff1a\u4eceStatsmodels\u83b7\u53d6\u56de\u5f52\u6a21\u578b\u6458\u8981<\/strong><\/span><\/h3>\n
\u5982\u679c\u4f60\u60f3\u5728Python\u4e2d\u63d0\u53d6\u56de\u5f52\u6a21\u578b\u7684\u6458\u8981\uff0c\u6700\u597d\u4f7f\u7528statsmodels<\/strong>\u5305\u3002<\/span><\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u6b64\u5305\u6765\u62df\u5408\u4e0e\u524d\u9762\u7684\u793a\u4f8b\u76f8\u540c\u7684\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u5e76\u63d0\u53d6\u6a21\u578b\u6458\u8981\uff1a<\/span><\/p>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df[' y<\/span> ']\n\n#define predictor variables\n<\/span>x = df[[' x1<\/span> ', ' x2<\/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: y R-squared: 0.767\nModel: OLS Adj. R-squared: 0.708\nMethod: Least Squares F-statistic: 13.15\nDate: Fri, 01 Apr 2022 Prob (F-statistic): 0.00296\nTime: 11:10:16 Log-Likelihood: -31.191\nNo. Comments: 11 AIC: 68.38\nDf Residuals: 8 BIC: 69.57\nDf Model: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 70.4828 3.749 18.803 0.000 61.839 79.127\nx1 5.7945 1.132 5.120 0.001 3.185 8.404\nx2 -1.1576 1.065 -1.087 0.309 -3.613 1.298\n==================================================== ============================\nOmnibus: 0.198 Durbin-Watson: 1.240\nProb(Omnibus): 0.906 Jarque-Bera (JB): 0.296\nSkew: -0.242 Prob(JB): 0.862\nKurtosis: 2.359 Cond. No. 10.7\n==================================================== ============================\n<\/strong><\/span><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u56de\u5f52\u7cfb\u6570\u548c R \u5e73\u65b9\u503c\u4e0e scikit-learn \u8ba1\u7b97\u7684\u7ed3\u679c\u76f8\u5339\u914d\uff0c\u4f46\u6211\u4eec\u8fd8\u6709\u5927\u91cf\u5176\u4ed6\u7528\u4e8e\u56de\u5f52\u6a21\u578b\u7684\u6709\u7528\u6307\u6807\u3002<\/span><\/p>\n
\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6bcf\u4e2a\u5355\u72ec\u9884\u6d4b\u53d8\u91cf\u7684 p \u503c\uff1a<\/span><\/p>\n
\u867d\u7136\u8fd9\u4e2a\u7ed3\u679c\u5f88\u6709\u7528\uff0c\u4f46\u6211\u4eec\u4ecd\u7136\u4e0d\u77e5\u9053\u6a21\u578b\u7684\u6574\u4f53 F \u7edf\u8ba1\u91cf<\/a>\u3001\u5404\u4e2a\u56de\u5f52\u7cfb\u6570<\/a>\u7684 p \u503c\u4ee5\u53ca\u5176\u4ed6\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6a21\u578b\u62df\u5408\u7a0b\u5ea6\u7684\u6709\u7528\u5ea6\u91cf\u3002\u6570\u636e\u96c6.\u6570\u636e\u96c6\u3002<\/span><\/p>\n
\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u4ece\u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570\u4ee5\u53ca\u6a21\u578b\u7684R \u5e73\u65b9\u503c<\/a>\uff1a<\/span><\/span><\/p>\n