{"id":1179,"date":"2023-07-27T09:36:52","date_gmt":"2023-07-27T09:36:52","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84k%e6%8a%98%e4%ba%a4%e5%8f%89%e9%aa%8c%e8%af%81\/"},"modified":"2023-07-27T09:36:52","modified_gmt":"2023-07-27T09:36:52","slug":"python%e4%b8%ad%e7%9a%84k%e6%8a%98%e4%ba%a4%e5%8f%89%e9%aa%8c%e8%af%81","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84k%e6%8a%98%e4%ba%a4%e5%8f%89%e9%aa%8c%e8%af%81\/","title":{"rendered":"Python \u4e2d\u7684 k \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff08\u9010\u6b65\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u4e3a\u4e86\u8bc4\u4f30\u6a21\u578b\u5728\u6570\u636e\u96c6\u4e0a\u7684\u6027\u80fd\uff0c\u6211\u4eec\u9700\u8981\u8861\u91cf\u6a21\u578b\u505a\u51fa\u7684\u9884\u6d4b\u4e0e\u89c2\u5bdf\u5230\u7684\u6570\u636e\u7684\u5339\u914d\u7a0b\u5ea6\u3002<\/span><\/p>\n

\u6267\u884c\u6b64\u64cd\u4f5c\u7684\u5e38\u7528\u65b9\u6cd5\u79f0\u4e3ak \u6298\u4ea4\u53c9\u9a8c\u8bc1<\/a>\uff0c\u5b83\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\uff1a<\/span><\/p>\n

1.<\/strong>\u5c06\u6570\u636e\u96c6\u968f\u673a\u5206\u4e3ak<\/em>\u7ec4\uff0c\u6216\u201c\u6298\u53e0\u201d\uff0c\u5927\u5c0f\u5927\u81f4\u76f8\u7b49\u3002<\/span><\/p>\n

2.<\/strong>\u9009\u62e9\u5176\u4e2d\u4e00\u4e2a\u6298\u53e0\u4f5c\u4e3a\u7ea6\u675f\u88c5\u7f6e\u3002\u5c06\u6a21\u677f\u8c03\u6574\u5230\u5269\u4f59\u7684 k-1 \u6298\u53e0\u3002\u6839\u636e\u5f20\u7d27\u5c42\u4e2d\u7684\u89c2\u6d4b\u503c\u8ba1\u7b97 MSE \u68c0\u9a8c\u3002<\/span><\/p>\n

3.<\/strong>\u91cd\u590d\u6b64\u8fc7\u7a0bk<\/em>\u6b21\uff0c\u6bcf\u6b21\u4f7f\u7528\u4e0d\u540c\u7684\u96c6\u5408\u4f5c\u4e3a\u6392\u9664\u96c6\u3002<\/span><\/p>\n

4.<\/strong>\u5c06\u603b\u4f53\u6d4b\u8bd5 MSE \u8ba1\u7b97\u4e3ak \u4e2a<\/em>\u6d4b\u8bd5 MSE \u7684\u5e73\u5747\u503c\u3002<\/span><\/p>\n

\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u5bf9\u7ed9\u5b9a\u6a21\u578b\u6267\u884c k \u6298\u4ea4\u53c9\u9a8c\u8bc1\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n

\u7b2c 1 \u6b65\uff1a\u52a0\u8f7d\u5fc5\u8981\u7684\u5e93<\/strong><\/span><\/h3>\n

\u9996\u5148\uff0c\u6211\u4eec\u5c06\u52a0\u8f7d\u6b64\u793a\u4f8b\u6240\u9700\u7684\u51fd\u6570\u548c\u5e93\uff1a<\/span><\/p>\n

 from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> KFold\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> cross_val_score\nfrom<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\nfrom<\/span> numpy import<\/span> means\nfrom<\/span> numpy import<\/span> absolute\nfrom<\/span> numpy import<\/span> sqrt\nimport<\/span> pandas as<\/span> pd\n<\/strong><\/span><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h3>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u4e24\u4e2a\u9884\u6d4b\u53d8\u91cfx1<\/sub>\u548cx2<\/sub>\u4ee5\u53ca\u4e00\u4e2a\u54cd\u5e94\u53d8\u91cf y\u3002<\/span><\/p>\n
 df = pd.DataFrame({' y<\/span> ': [6, 8, 12, 14, 14, 15, 17, 22, 24, 23],\n                   ' x1<\/span> ': [2, 5, 4, 3, 4, 6, 7, 5, 8, 9],\n                   ' x2<\/span> ': [14, 12, 12, 13, 7, 8, 7, 4, 6, 5]})\n<\/strong><\/span><\/pre>\n
\u6b65\u9aa4 3\uff1a\u6267\u884c K \u6298\u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/span><\/h3>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u5bf9\u6570\u636e\u96c6\u62df\u5408 \u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\uff0c\u5e76\u6267\u884c LOOCV \u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 10<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span>\n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use k-fold CV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_absolute_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view mean absolute error\n<\/span>mean(absolute(scores))\n\n3.6141267491803646\n<\/strong><\/span><\/pre>\n
\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\uff08MAE\uff09\u4e3a3.614<\/strong> \u3002\u5373\u6a21\u578b\u9884\u6d4b\u4e0e\u5b9e\u9645\u89c2\u6d4b\u6570\u636e\u4e4b\u95f4\u7684\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\u4e3a3.614\u3002<\/span><\/p>\n
\u4e00\u822c\u6765\u8bf4\uff0cMAE \u8d8a\u4f4e\uff0c\u6a21\u578b\u9884\u6d4b\u5b9e\u9645\u89c2\u6d4b\u503c\u7684\u80fd\u529b\u5c31\u8d8a\u597d\u3002<\/span><\/p>\n
\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u53e6\u4e00\u4e2a\u5e38\u7528\u6307\u6807\u662f\u5747\u65b9\u6839\u8bef\u5dee (RMSE)\u3002\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u4f7f\u7528 LOOCV \u8ba1\u7b97\u6b64\u6307\u6807\uff1a<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 5<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span> \n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use LOOCV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_squared_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view RMSE\n<\/span>sqrt(mean(absolute(scores)))\n\n4.284373111711816<\/strong><\/span><\/pre>\n
\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u4e3a4.284<\/strong> \u3002<\/span><\/p>\n
 RMSE \u8d8a\u4f4e\uff0c\u6a21\u578b\u9884\u6d4b\u5b9e\u9645\u89c2\u6d4b\u503c\u7684\u80fd\u529b\u5c31\u8d8a\u597d\u3002<\/span><\/p>\n
\u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u4f1a\u62df\u5408\u51e0\u4e2a\u4e0d\u540c\u7684\u6a21\u578b\uff0c\u5e76\u6bd4\u8f83\u6bcf\u4e2a\u6a21\u578b\u7684 RMSE \u6216 MAE\uff0c\u4ee5\u786e\u5b9a\u54ea\u4e2a\u6a21\u578b\u4ea7\u751f\u6700\u4f4e\u7684\u6d4b\u8bd5\u9519\u8bef\u7387\uff0c\u56e0\u6b64\u662f\u6700\u597d\u4f7f\u7528\u7684\u6a21\u578b\u3002<\/span><\/p>\n
\u53e6\u8bf7\u6ce8\u610f\uff0c\u5728\u672c\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9009\u62e9\u4f7f\u7528 k=5 \u6298\u53e0\uff0c\u4f46\u60a8\u53ef\u4ee5\u9009\u62e9\u6240\u9700\u7684\u4efb\u610f\u6298\u53e0\u6b21\u6570\u3002<\/span><\/p>\n
\u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u9009\u62e9 5 \u5230 10 \u5c42\u4e4b\u95f4\uff0c\u56e0\u4e3a\u4e8b\u5b9e\u8bc1\u660e\u8fd9\u662f\u4ea7\u751f\u53ef\u9760\u6d4b\u8bd5\u9519\u8bef\u7387\u7684\u6700\u4f73\u5c42\u6570\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
K \u6298\u4ea4\u53c9\u9a8c\u8bc1\u7b80\u4ecb<\/a>
Python \u7ebf\u6027\u56de\u5f52\u5b8c\u6574\u6307\u5357<\/a>
Python \u4e2d\u7684\u7559\u4e00\u4ea4\u53c9\u9a8c\u8bc1<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u4e3a\u4e86\u8bc4\u4f30\u6a21\u578b\u5728\u6570\u636e\u96c6\u4e0a\u7684\u6027\u80fd\uff0c\u6211\u4eec\u9700\u8981\u8861\u91cf\u6a21\u578b\u505a\u51fa\u7684\u9884\u6d4b\u4e0e\u89c2\u5bdf\u5230\u7684\u6570\u636e\u7684\u5339\u914d\u7a0b\u5ea6\u3002 \u6267\u884c\u6b64\u64cd\u4f5c\u7684\u5e38\u7528\u65b9\u6cd5\u79f0\u4e3ak […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1179","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\nPython \u4e2d\u7684 K \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff08\u4e00\u6b65\u4e00\u6b65\uff09- Statory<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c k \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u5305\u62ec\u5206\u6b65\u793a\u4f8b\u3002\" \/>\n<meta name=\"robots\" content=\"index, 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