{"id":3695,"date":"2022-04-15T01:21:14","date_gmt":"2022-04-15T09:51:14","guid":{"rendered":"http:\/\/10ccc.cc\/?p=3695"},"modified":"2026-01-02T20:47:43","modified_gmt":"2026-01-02T12:47:43","slug":"%e3%80%90flag%e3%80%91%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0100%e5%a4%a9-%e7%ac%ac2%e5%a4%a9-%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92","status":"publish","type":"post","link":"https:\/\/10ccc.cc\/?p=3695","title":{"rendered":"\u3010Flag\u3011\u673a\u5668\u5b66\u4e60100\u5929-[\u7b2c2\u5929]-\u7ebf\u6027\u56de\u5f52"},"content":{"rendered":"

\u5728\u672c\u7bc7\u5185\u5bb9\u4e2d\uff0c\u6211\u4eec\u5c06\u4f1a\u8fdb\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u7684\u5b9e\u73b0\uff0c\u5c1d\u8bd5\u901a\u8fc7\u6570\u636e\u96c6studentscores\u4e2d\u5b66\u751f\u7684\u5b66\u4e60\u5c0f\u65f6\u6570 Hours \u6765\u9884\u6d4b\u5b66\u751f\u7684\u8003\u8bd5\u5f97\u5206 Scores .<\/p><\/blockquote>\n

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\u672c\u7bc7\u5185\u5bb9\u7684\u7b14\u8bb0\u9879\u76ee\u7ed3\u6784\u5982\u4e0b\u56fe\u6240\u793a\u3002\u51c6\u5907\u5c31\u7eea\uff0c\u524d\u4e00\u5929\u5185\u5bb9\u8bf7\u67e5\u9605\u8fd9\u91cc<\/a><\/p>\n

\"\"<\/p>\n

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\u5bfc\u5165\u6570\u636e\u96c6\u5e76\u5207\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/h2>\n

\u672c\u6b21\u7684\u6d4b\u8bd5\u96c6\u5927\u5c0f\u4e3a\u6570\u636e\u96c6\u768425%<\/strong>, \u91cd\u590d\u4ee3\u7801\u7684\u89e3\u91ca\u53ef\u5728 day_1<\/a> \u4e2d\u67e5\u770b\uff0c\u8fd9\u91cc\u5c31\u4e0d\u518d\u8d58\u8ff0<\/p><\/blockquote>\n

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\u6253\u5f00 studentscores.csv\u3002\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u8fd9\u662f 25 \u00d7 2 \u7684\u6570\u636e\u3002 \u77e5\u6653\u4e86\u6570\u636e\u7684\u57fa\u672c\u7ed3\u6784\u4e4b\u540e\uff0c\u6211\u4eec\u63a5\u4e0b\u6765\u5c06\u901a\u8fc7 sklearn \u6765\u5bf9\u8fd9\u4e9b\u6570\u636e\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\u7684\u62df\u5408\u64cd\u4f5c\u3002<\/p>\n

\"\u6570\u636e\u96c6\u539f\u6587\u4ef6\"
25 x 2 \u6570\u636e\u96c6<\/figcaption><\/figure>\n

\u9996\u5148\uff0c\u6211\u4eec\u901a\u8fc7\u4e0b\u65b9\u547d\u4ee4\uff0c\u5c06\u8bfb\u53d6\u6570\u636e\u96c6\u5e76\u5c06\u5176\u62c6\u5206\u6210\u8bad\u7ec3\u96c6<\/strong>\u4ee5\u53ca\u6d4b\u8bd5\u96c6<\/strong>\u3002\u5e76\u67e5\u770b\u62c6\u5206\u540e\u7684\u7ed3\u679c\u3002<\/p>\n

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\n \n Copy Code<\/span>\n Copied<\/span>\n Use a different Browser<\/span><\/a>\n <\/div>\n <\/div>\n 
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import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndataset = pd.read_csv('studentscores.csv')\r\nX = dataset.iloc[ : , : 1 ].values\r\nY = dataset.iloc[ : , 1 ].values\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1\/4, random_state = 0)\r\n\r\nprint(\"X_train:\", X_train)\r\nprint(\"X_test:\", X_test)\r\nprint(\"Y_train:\", Y_train)\r\nprint(\"Y_test:\", Y_test)<\/pre>\n
<\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
\u5bfc\u5165\u6570\u636e\u8fd0\u884c\u7ed3\u679c\u5982\u4e0b\u56fe\u6240\u793a\uff1a<\/p>\n
\"\u6570\u636e\u96c6\u62c6\u5206\u7ed3\u679c\"
\u5c06\u6570\u636e\u96c6\u62c6\u5206\u6210 \u8bad\u7ec3\u96c6 \u548c \u6d4b\u8bd5\u96c6\uff0c\u6bd4\u4f8b\u4e3a3:1<\/figcaption><\/figure>\n
 <\/p>\n
\u4f7f\u7528\u8bad\u7ec3\u96c6X_train, Y_train \u8fdb\u884c\u7ebf\u6027\u56de\u5f52<\/h2>\n
\n\t\t\t
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\n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
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from sklearn.linear_model import LinearRegression\r\nregressor = LinearRegression()\r\nregressor = regressor.fit(X_train, Y_train)\r\n\r\nprint(regressor.coef_)<\/pre>\n
<\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
\u8f93\u51fa\u7ed3\u679c\u56de\u5f52\u7cfb\u6570coefficient\uff1a[9.94167834]<\/p><\/blockquote>\n
 <\/p>\n
\u5176\u4e2d\u00a0 regressor.fit(X_train, Y_train)<\/strong>\u00a0<\/em> \u65b9\u6cd5\u5219\u662f\u5728\u5bf9X_train\uff0c Y_train<\/em> \u8fdb\u884c\u7ebf\u6027\u62df\u5408\u3002 \u6ce8\u610f\uff0csklearn \u5728\u6b64\u5904\u8fdb\u884c\u62df\u5408\u65f6\u91c7\u7528\u7684\u662f \u6700\u5c0f\u4e8c\u4e58\u6cd5<\/em><\/strong>\uff0c \u800c\u975e \u68af\u5ea6\u4e0b\u964d\u6cd5<\/em><\/strong> \u3002<\/p>\n
 <\/p>\n
\u6570\u636e\u53ef\u89c6\u5316<\/h2>\n
\u4f7f\u7528\u8bad\u7ec3\u96c6X_train, Y_train \u6570\u636e\u67e5\u770b\u56de\u5f52\u65b9\u7a0b\u62df\u5408\u6548\u679c<\/h3>\n
\u4f7f\u7528matplotlib\u7ed8\u5236\u6563\u70b9\u56fe\u53ca\u51fd\u6570\u56fe\u50cf\uff0c\u8fdb\u884c\u6d4b\u8bd5\u96c6\u6570\u636e\u53ef\u89c6\u5316<\/p>\n
\n\t\t\t
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<\/p>\n
plt.scatter(X_train , Y_train, color = 'red')\r\nplt.plot(X_train , regressor.predict(X_train), color ='blue')<\/pre>\n
<\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
\"\"<\/p>\n
 <\/p>\n
 <\/p>\n
\u4f7f\u7528\u6d4b\u8bd5\u96c6Y_test, X_test \u6570\u636e\u68c0\u9a8c\u56de\u5f52\u65b9\u7a0bregressor<\/h3>\n
\n\t\t\t
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\n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
<\/p>\n
Y_pred = regressor.predict(X_test)\r\nprint(Y_pred)<\/pre>\n
<\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
\u7cbe\u786e\u503c\uff1a<\/p>\n
\"\"<\/p>\n
regressor\u8ba1\u7b97\u503c\uff1a<\/p>\n
\"\"<\/p>\n
\u4f7f\u7528matplotlib\u7ed8\u5236\u6563\u70b9\u56fe\u53ca\u51fd\u6570\u56fe\u50cf\uff0c\u8fdb\u884c\u6d4b\u8bd5\u96c6\u6570\u636e\u53ef\u89c6\u5316\uff1a<\/p>\n
\n\t\t\t
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\n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
<\/p>\n
plt.scatter(X_test , Y_test, color = 'red')
\n# plt.plot(X_test , regressor.predict(X_test), color ='blue')
\nprint(\"===\"*5)
\nplt.plot(X_test , Y_pred, color ='blue')<\/p>\n
<\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
\"\"<\/p>\n
 <\/p>\n
\u8865\u5145\u5185\u5bb9<\/h2>\n
\u7ebf\u6027\u56de\u5f52\u539f\u7406<\/h3>\n
\u5728\u672c\u6587\u4e2d\uff0c\u5f53\u6211\u4eec\u8c08\u5230\u7ebf\u6027\u56de\u5f52\u65f6\uff0c\u6211\u4eec\u5b9e\u9645\u4e0a\u5728\u8bf4\u7684\u662f\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u3002\u5c31\u662f\u5f53\u6211\u4eec\u77e5\u9053\u4e8b\u7269\u7684\u4e00\u4e2a\u6027\u8d28\u65f6\uff0c\u53ef\u4ee5\u77e5\u9053\u53e6\u4e00\u4e2a\u786e\u5b9a\u7684\u6027\u8d28\u3002\u5373\uff1a\u4e00\u822c\u6765\u8bf4\u6211\u4eec\u4f1a\u901a\u8fc7\u4e00\u4e2a\u5b9a\u8ddd\u53d8\u91cf\u53bb\u9884\u6d4b\u53e6\u4e00\u4e2a\u5b9a\u8ddd\u53d8\u91cf\uff0c\u5982\u4e0b\u65b9\u7684\u4f8b\u5b50\u3002<\/p>\n
\u4f8b\u5982\uff1a\u6211\u4eec\u77e5\u9053\u5c0f\u660e\u6709\u4e00\u8f86\u4e09\u8f6e\u81ea\u884c\u8f66\uff0c\u4ed6\u4f1a\u8e29\u81ea\u884c\u8f66\u8f7d\u4ed6\u670b\u53cb\u3002\u5f53\u4ed6\u8f7d1\u4e2a\u670b\u53cb\u65f6 \uff0c\u8e29\u7684\u901f\u5ea6\u80fd\u8fbe\u5230100km\/h,\uff1b\u8f7d\u4e24\u4e2a\u670b\u53cb\u65f6\uff0c\u8e29\u7684\u901f\u5ea6\u53ea\u80fd\u523090km\/h......<\/p>\n
\"\"<\/p>\n
\u4f46\u6709\u65f6\u6709\u6211\u4eec\u4e5f\u53ef\u4ee5\u901a\u8fc7\u5bf9\u4e00\u4e2a\u5b9a\u8ddd\u53d8\u91cf\u7684\u89c2\u5bdf\uff0c\u53bb\u9884\u6d4b\u4e00\u4e2a\u5b9a\u7c7b\u53d8\u91cf\u7684\u7ed3\u679c\uff0c\u5982\u901a\u8fc7\u5e73\u5747\u4f53\u91cd\u53bb\u9884\u6d4b\u6210\u5e74\u7537\u5973\u6027\u522b<\/p><\/blockquote>\n
\u6240\u4ee5\uff0c\u5982\u679c\u4f60\u8db3\u591f\u654f\u9510\uff0c\u5c31\u53ef\u4ee5\u53d1\u73b0\uff0c\u5728\u67d0\u4e9b\u5408\u9002\u7684\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u5408\u9002\u7684\u6570\u91cf\u9608\u503c\uff0c\u4ece\u800c\u5c06\u5206\u7c7b\u95ee\u9898\u8f6c\u5316\u4e3a\u7ebf\u6027\u56de\u5f52\u95ee\u9898<\/em><\/strong>\u3002\u4f8b\u5982\u7684\u6210\u5e74\u7537\u5973\u6027\u522b\u9884\u6d4b\u7684\u4f8b\u5b50\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u5e73\u5747\u4f53\u91cd\u5728\u533a\u95f4 [50KG, 60KG) \u4e4b\u95f4\u7684\u5b9a\u4e49\u4e3a\u6210\u5e74\u5973\u6027\uff0c\u5728\u533a\u95f4 [60KG, 90KG) \u8303\u56f4\u5185\u7684\u5b9a\u4e49\u4e3a\u6210\u5e74\u7537\u6027\u3002 \u4f46\u5b9e\u9645\u4e0a\uff0c\u8fd9\u79cd\u5206\u7c7b\u95ee\u9898\uff0c\u4e00\u822c\u4f7f\u7528\u903b\u8f91\u56de\u5f52<\/strong><\/em>\u3002<\/p>\n
 <\/p>\n
\u4e00\u5143\u7ebf\u6027\u56de\u5f52<\/h3>\n
\u6211\u4eec\u77e5\u9053\u4e8c\u5143\u4e00\u6b21\u65b9\u7a0b<\/a>\u3002\u6211\u4eec\u4e00\u822c\u8bbe\u7f6ey\u4e3a\u56e0\u53d8\u91cf\uff0cx\u4e3a\u81ea\u53d8\u91cf\u3002\u5bf9\u4e8e\u7ed9\u5b9a\u7684\u03b1 \uff0c\u03b2\uff1b<\/p>\n
\"\"<\/p>\n
\u4e0a\u65b9\u7684\u516c\u5f0f\u5728\u76f4\u89d2\u5750\u6807\u7cfb\u4e2d\uff0c\u662f\u4e00\u6761\u7279\u5b9a\u7684\u76f4\u7ebf\uff0c\u8fd9\u5c31\u662f\u4e3a\u4ec0\u4e48\u6211\u4eec\u4f1a\u5c06\u5176\u6210\u4e3a\u3010\u7ebf\u6027<\/strong>\u3011\u56de\u5f52\u7684\u539f\u56e0\u3002<\/p>\n
 <\/p>\n
\u800c\u5728\u4e0a\u65b9\u516c\u5f0f\u4e2d\uff0c\u56e0\u4e3a\u6211\u4eec\u662f\u7528\u4e00\u4e2a\u81ea\u53d8\u91cf\u53d8\u91cf x \u6765\u9884\u6d4b\u5e94\u53d8\u91cf y\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u6211\u4eec\u5728\u6254\u4e86\u4e00\u6761\u76f4\u7ebf\u5728<\/strong>\u4e00\u5806\u6570\u636e\u70b9\u4e2d\uff0c\u5e76\u4e0d\u65ad\u4e0a\u4e0b\u79fb\u52a8(\u8c03\u6574\u03b2)\u3001\u65cb\u8f6c(\u8c03\u6574\u03b1)\u8fd9\u6761\u76f4\u7ebf\u6765\u62df\u5408\u6570\u636e\u3002<\/p>\n
\u5982\u5728\u4eca\u5929\u7684\u6570\u636e\u96c6\u4e2d\uff0c\u6211\u4eec\u5c06\u5b66\u751f\u5b66\u4e60\u5c0f\u65f6\u6570\u548c\u6700\u7ec8\u8003\u8bd5\u7ed3\u679c\u5206\u6570\u7684\u6570\u636e\u70b9\u5728\u5750\u6807\u4e2d\u6807\u6ce8\u51fa\u6765\u3002\u5176\u4e2d\uff0c\u6a2a\u5750\u6807\u4e3a\u5b66\u4e60\u5c0f\u65f6\u6570\uff0c\u7eb5\u5750\u6807\u4e3a\u8003\u8bd5\u5206\u6570<\/strong>\u3002<\/p>\n
\"\u5b66\u4e60\u5c0f\u65f6\u6570\u4e0e\u5206\u6570\u4e4b\u95f4\u5173\u7cfb\"
\u5b66\u4e60\u5c0f\u65f6\u6570\u4e0e\u5206\u6570\u4e4b\u95f4\u5173\u7cfb<\/figcaption><\/figure>\n
 <\/p>\n
\u6211\u4eec\u89c2\u5bdf\u4e0a\u56fe\uff0c\u7ea2\u70b9\u90e8\u5206\u8868\u793a\u7684\u662f\u201c\u4e0d\u51c6\u786e\u201d\u7684\u73b0\u5b9e\u4e16\u754c\u4e2d\u7684\u6570\u636e\uff0c\u800c\u84dd\u7ebf\u5219\u662f\u6211\u4eec\u671f\u671b\u627e\u5230\u7684\u201c\u51c6\u786e\u5173\u7cfb\u201d(\u8fd9\u91cc\u6211\u5077\u61d2\u4e86\uff0c\u76f4\u63a5\u7528\u4e86\u8ba1\u7b97\u51fa\u6765\u7684\u51fd\u6570<\/del>)\uff0c\u5373\uff0c\u6211\u4eec\u8f93\u5982\u4e00\u4e2a\u5b66\u4e60\u5c0f\u65f6\u6570\uff0c\u80fd\u591f\u8fd4\u56de\u4e00\u4e2a\u51c6\u786e\u4e14\u552f\u4e00\u7684\u5206\u6570\u3002\u4f46\u6211\u4eec\u4e0d\u96be\u53d1\u73b0\uff0c\u6bcf\u4e2a\u786e\u5b9a\u7684\u73b0\u5b9e\u7ea2\u70b9\u4e0e\u201c\u5173\u7cfb\u201d\u4e4b\u95f4\u4f3c\u4e4e\u603b\u662f\u4e0d\u80fd\u5b8c\u7f8e\u5730\u201c\u8d34\u5408\u201d\uff0c\u4e5f\u5c31\u662f\u8bf4\uff0c\u6211\u4eec\u7528\u5b58\u5728\u7684\u67d0\u4e2a\u201c\u51c6\u786e\u5173\u7cfb<\/strong>\u201d\u8ba1\u7b97\u51fa\u6765\u7684\u503c\uff08\u84dd\u7ebf\u4e0a\u7684\u503c\uff09\u4e0e\u5b9e\u9645\u7ea2\u70b9\u7684\u503c\u603b\u4f1a\u201c\u4e0d\u53ef\u907f\u514d\u201d\u5730\u5b58\u5728\u5dee\u503c\uff0c\u5373 \u8bef\u5dee\u3002\u90a3\u4e48\u6211\u4eec\u53ef\u5728\u539f\u6709\u7684\u7ebf\u6027\u65b9\u7a0b\u4e2d\u6dfb\u52a0\u4e00\u4e2a\u53c2\u6570 \u03b5 \u6765\u63cf\u8ff0\u8fd9\u79cd\u5dee\u8ddd\uff1a<\/strong><\/p>\n
\"\u6dfb\u52a0\u8bef\u5dee\u9879\"
\u6dfb\u52a0\u8bef\u5dee\u9879<\/figcaption><\/figure>\n
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 <\/p>\n
\u635f\u5931\u538c\u6076\u548c\u635f\u5931\u51fd\u6570<\/h3>\n
\u57fa\u672c\u903b\u8f91<\/h4>\n
\u6b64\u65f6\uff0c\u6211\u4eec\u4fee\u6539\u610f\u601d\u4e0b\u516c\u5f0f\uff0c\u6ce8\u610f\u4e0b\u6807 i \uff1a<\/p>\n
\"\"<\/p>\n
\u4e4b\u524d\u6211\u4eec\u63d0\u5230\uff0c\u663e\u793a\u6570\u636e\u4e0e\u62df\u5408\u66f2\u7ebf\u4e4b\u95f4\u603b\u662f\u5b58\u5728\u8bef\u5dee\u3002\u90a3\u4e48\u6211\u4eec\u5c31\u5e0c\u671b\u6211\u4eec\u62df\u5408\u51fa\u6765\u7684\u66f2\u7ebf\u662f\u4e0e\u771f\u5b9e\u6570\u636e\u4e4b\u95f4\u7684\u8bef\u5dee \u03b5 \u6700\u5c0f\u7684\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u5c1d\u8bd5\u4e0d\u65ad\u8c03\u6574\u03b1 \u548c \u03b2\u3002\u6765\u8ba9\u66f2\u7ebf\u62df\u5408\u7684\u503c\u4e0e\u771f\u5b9e\u6570\u636e\u503c\u4e4b\u95f4\u7684\u8bef\u5dee\u5c3d\u53ef\u80fd\u5c0f\u3002\u540c\u65f6\uff0c\u6211\u4eec\u4e5f\u5f88\u5bb9\u6613\u53d1\u73b0\uff0c\u65e0\u8bba\u6211\u4eec\u5bf9 \u03b1 \u548c \u03b2\u600e\u4e48\u8c03\u6574\uff0c\u7531\u4e8e\u4e00\u5143\u4e00\u6b21\u51fd\u6570\u662f\u76f4\u7ebf\u3002\u4e5f\u5c31\u610f\u4e3a\u8fd9\u5bf9\u67d0\u4e2a\u70b9\u7684\u8bef\u5dee\u53d8\u5c0f\u7684\u65f6\u5019\uff0c\u5bf9\u53e6\u5916\u7684\u70b9\u7684\u8bef\u5dee\u5219\u53ef\u80fd\u589e\u5927\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u8f6c\u800c\u603b\u89c8\u5168\u5c40\uff0c\u4e0d\u8981\u6c42\u6211\u4eec\u7684\u51fd\u6570\u5bf9\u67d0\u4e2a\u6570\u636e\u70b9\u7684\u8bef\u5dee\u6700\u5c0f\uff0c\u3010\u4f46\u8981\u6c42\u5b83\u4e0e\u6240\u6709\u7684\u6570\u636e\u70b9\u7684\u8bef\u5dee\u7684\u548c\u6700\u5c0f<\/strong>\u3002\u3011<\/p>\n
 <\/p>\n
\u5373\uff1a\"\"<\/p><\/blockquote>\n
\u6b64\u5904\uff0c\u6211\u4eec\u7ea6\u5b9a\"\"\u4e3a\u771f\u5b9e\u503c\uff0c\u00a0\"\"\u4e3a\u9884\u6d4b\u503c\u3002\u5373\uff0c\u6211\u4eec\u7528\u6bcf\u4e00\u4e2a\u70b9\u7684\u771f\u5b9e\u503c\u51cf\u53bb\u9884\u6d4b\u503c\uff0c\u5f97\u5230\u4e00\u4e2a\u70b9\u7684\u8bef\u5dee\uff0c\u5728\u5c06\u6240\u6709\u6570\u636e\u70b9\u7684\u8bef\u5dee\u7d2f\u52a0\uff0c\u5373\u53ef\u7b97\u51fa\u8bef\u5dee\u7684\u548c\u3002\u4f46\uff0c\u8fd9\u6837\u505a\u7684\u53ef\u80fd\u5b58\u5728\u6b63\u8d1f\u503c\u4e92\u76f8\u62b5\u6d88\u7684\u60c5\u51b5\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u8c03\u6574\u4e0b\u516c\u5f0f\u5e76\u8fdb\u884c\u63a8\u5bfc, \u5f97\u5230\u635f\u5931\u51fd\u6570\uff1a<\/p>\n
\"\u8bef\u5dee\u5e73\u65b9\u548cSUM
\u8bef\u5dee\u5e73\u65b9\u548cSUM OF SQURE ERRORs<\/figcaption><\/figure>\n
 <\/p>\n
\u635f\u5931\u51fd\u6570\u6700\u5c0f\u503c<\/h4>\n
\u5df2\u7ecf\u77e5\u9053\u90a3\u4e48\u6211\u4eec\uff0c\u6211\u4eec\u73b0\u5728\u7684\u4efb\u52a1\u5c31\u662f\u6c42\u51fa\u53ef\u4ee5\u8ba9\u635f\u5931\u51fd\u6570\u8fbe\u5230\u6700\u5c0f\u503c\u7684\u70b9(\u03b1, \u03b2)\uff1b\u6ce8\u610f\u5230\u635f\u5931\u51fd\u6570\uff1a<\/p>\n
\"\u635f\u5931\u51fd\u6570\"
\u635f\u5931\u51fd\u6570<\/figcaption><\/figure>\n
\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u6ce8\u610f\u5230\u03b1, \u03b2\u662f\u4e24\u4e2a\u672a\u77e5\u53d8\u91cf\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u901a\u8fc7\u5148\u6c42\u504f\u5bfc(\u6ce8\u610f\u94fe\u5f0f\u6cd5\u5219\u7684\u4f7f\u7528)\uff0c\u5e76\u4f7f\u5176\u4e3a0<\/strong>\uff0c\u518d\u5c06\u6240\u6709\u5b9e\u9645\u503c\u4ee3\u5165\u504f\u5bfc\u6570\uff0c\u6700\u540e\u6c42\u89e3\u65b9\u7a0b\u7ec4\u7684\u65b9\u5f0f\u8ba1\u7b97\u51fa\u6211\u4eec\u6240\u9700\u7684\u03b1\u548c\u03b2\u503c\u3002<\/p>\n
\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u504f\u5bfc:<\/p>\n
\"\u635f\u5931\u51fd\u6570\u504f\u5bfc\"
\u635f\u5931\u51fd\u6570\u504f\u5bfc<\/figcaption><\/figure>\n
 <\/p>\n
\u73b0\u5728\u6211\u4eec\u5c06\u8bad\u7ec3\u96c6\u4e2d\u7684\u6570\u636e\u5206\u522b\u4ee3\u5165\"\"\u548c\"\"\u8ba1\u7b97\u635f\u5931\u51fd\u6570\u504f\u5bfc(\u6ce8\u610f\uff0c\u6839\u636e\u4e0a\u6587\u903b\u8f91\uff0c\u8bad\u7ec3\u96c6\u7684\u6bcf\u4e00\u7ec4\u6570\u636e\u90fd\u80fd\u8ba9\u5bf9\u5e94\u7684\u504f\u5bfc\u6570\u503c\u4e3a0)\uff0c\u7531\u4e8e\u8ba1\u7b97\u91cf\u6bd4\u8f83\u5927\uff0c\u6211\u4eec\u901a\u8fc7gridstudio\u8ba1\u7b97()\u3002<\/p>\n
\"Gridstudio\"
Gridstudio<\/figcaption><\/figure>\n
\u57fa\u672c\u8ba1\u7b97\u903b\u8f91\u662f\u5c06\u5b9e\u9645\u89c2\u6d4b\u503c\u4ee3\u5165\u504f\u5bfc\u6570\uff0c\u5f97\u523018\uff08\u53e6\u03b1\u4e3a0\u7684\u65b9\u7a0b\u7ec4\uff09 + 18\uff08\u53e6\u03b2\u4e3a0\u7684\u65b9\u7a0b\u7ec4\uff09\u7ec4\u65b9\u7a0b\uff0c\u5e76\u5c06\u6bcf\u4e2a\u65b9\u7a0b\u7ec4\u7684\u5bf9\u5e94\u9879\u7d2f\u52a0\u3002\u6700\u7ec8\u5230\u4e24\u4e2a\u65b9\u7a0b(\u8bf7\u81ea\u884c\u5ffd\u7565\u6d6e\u70b9\u6570\u7684\u95ee\u9898)\uff1a<\/p>\n
Equation 1 : 2040.0 - 36\u03b2 - 198.20000000000005\u03b1 = 0<\/div>\n
Equation 2 :13400.0 - 198.20000000000005\u03b2 - 1309.3400000000001\u03b1 = 0<\/div>\n<\/blockquote>\n
 <\/p>\n
\u6700\u7ec8\u89e3\u65b9\u7a0b\u5f97\u5230\u5bf9\u5e94\u7684\u03b1, \u03b2\u503c\uff1a<\/p>\n
\u03b1: -0.413849232064970\uff1b\u03b2: 10.2968097803437<\/p><\/blockquote>\n
 <\/p>\n
\u5230\u8fd9\u4e00\u6b65\uff0c\u6211\u4eec\u5b8c\u6210\u4e86\u4ee3\u7801 regressor = regressor.fit(X_train, Y_train) \u80cc\u540e\u7684\u903b\u8f91\uff0c\u4e5f\u5c31\u771f\u6b63\u5b66\u660e\u767d\u4e86\u201c\u4eca\u5929(day_2)\u201d\u7684\u5185\u5bb9\u3002 \u7ed9\u81ea\u5df1\u6765\u676f\u5496\u5561\u5956\u52b1\u4e0b\u81ea\u5df1\u5427\u3002<\/p>\n
 <\/p>\n
gridstudio \u53ca\u5206\u6790\u4ee3\u7801\u4e0b\u8f7d\uff1a<\/strong><\/p>\n
gridstudio<\/strong>\uff1agithub<\/a><\/p>\n
manual_linear_process.py \u4e0b\u8f7d\uff1a<\/strong> \r\n<\/i>Download<\/span><\/a><\/p>\n
\u7b14\u8bb0\u4e0b\u8f7d<\/h2>\n
day_2.zip \uff1a<\/strong>\r\n<\/i>Download<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u5728\u672c\u7bc7\u5185\u5bb9\u4e2d\uff0c\u6211\u4eec\u5c06\u4f1a\u8fdb\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u7684\u5b9e\u73b0\uff0c\u5c1d\u8bd5\u901a\u8fc7\u6570\u636e\u96c6studentscores\u4e2d\u5b66\u751f\u7684\u5b66\u4e60\u5c0f\u65f6\u6570 Hours \u6765\u9884\u6d4b\u5b66\u751f\u7684 …<\/p>\n","protected":false},"author":1,"featured_media":3697,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[82],"tags":[105,107,106],"class_list":["post-3695","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-82","tag-105","tag-107","tag-106"],"_links":{"self":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts\/3695","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3695"}],"version-history":[{"count":5,"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts\/3695\/revisions"}],"predecessor-version":[{"id":4523,"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts\/3695\/revisions\/4523"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/media\/3697"}],"wp:attachment":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3695"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3695"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3695"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}