{"id":477831,"date":"2023-08-09T09:21:11","date_gmt":"2023-08-09T09:21:11","guid":{"rendered":""},"modified":"2023-09-05T11:15:32","modified_gmt":"2023-09-05T11:15:32","slug":"linear-regression","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/linear-regression\/","title":{"rendered":"\u7ebf\u6027\u56de\u5f52"},"content":{"rendered":"<p>\u7ebf\u6027\u56de\u5f52\u662f\u4e00\u79cd\u57fa\u672c\u7edf\u8ba1\u65b9\u6cd5\uff0c\u7528\u4e8e\u5bf9\u56e0\u53d8\u91cf\u4e0e\u4e00\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u8fdb\u884c\u5efa\u6a21\u3002\u5b83\u662f\u4e00\u79cd\u7b80\u5355\u800c\u5f3a\u5927\u7684\u6280\u672f\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u7ecf\u6d4e\u3001\u91d1\u878d\u3001\u5de5\u7a0b\u3001\u793e\u4f1a\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60\u7b49\u5404\u4e2a\u9886\u57df\u3002\u8be5\u65b9\u6cd5\u65e8\u5728\u627e\u5230\u6700\u9002\u5408\u6570\u636e\u70b9\u7684\u7ebf\u6027\u65b9\u7a0b\uff0c\u4f7f\u6211\u4eec\u80fd\u591f\u505a\u51fa\u9884\u6d4b\u5e76\u7406\u89e3\u6570\u636e\u4e2d\u7684\u6f5c\u5728\u6a21\u5f0f\u3002<\/p>\n<h2>\u7ebf\u6027\u56de\u5f52\u7684\u8d77\u6e90\u5386\u53f2\u53ca\u5176\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>\u7ebf\u6027\u56de\u5f52\u7684\u8d77\u6e90\u53ef\u4ee5\u8ffd\u6eaf\u5230 19 \u4e16\u7eaa\u521d\uff0c\u5f53\u65f6\u5361\u5c14\u00b7\u5f17\u91cc\u5fb7\u91cc\u5e0c\u00b7\u9ad8\u65af\u548c\u963f\u5fb7\u91cc\u5b89\u00b7\u9a6c\u91cc\u00b7\u52d2\u8ba9\u5fb7\u9996\u6b21\u5c06\u8be5\u65b9\u6cd5\u5e94\u7528\u4e8e\u5929\u6587\u5b66\u3002\u9ad8\u65af\u5f00\u53d1\u4e86\u6700\u5c0f\u4e8c\u4e58\u6cd5\uff0c\u8fd9\u662f\u7ebf\u6027\u56de\u5f52\u7684\u57fa\u77f3\uff0c\u7528\u4e8e\u5206\u6790\u5929\u6587\u6570\u636e\u548c\u4f30\u8ba1\u5929\u4f53\u7684\u8f68\u9053\u3002\u540e\u6765\uff0c\u52d2\u8ba9\u5fb7\u72ec\u7acb\u5e94\u7528\u4e86\u7c7b\u4f3c\u7684\u6280\u672f\u6765\u89e3\u51b3\u786e\u5b9a\u5f57\u661f\u8f68\u9053\u7684\u95ee\u9898\u3002<\/p>\n<h2>\u6709\u5173\u7ebf\u6027\u56de\u5f52\u7684\u8be6\u7ec6\u4fe1\u606f<\/h2>\n<p>\u7ebf\u6027\u56de\u5f52\u662f\u4e00\u79cd\u7edf\u8ba1\u5efa\u6a21\u6280\u672f\uff0c\u5047\u8bbe\u56e0\u53d8\u91cf\uff08\u901a\u5e38\u8868\u793a\u4e3a\u201cY\u201d\uff09\u548c\u81ea\u53d8\u91cf\uff08\u901a\u5e38\u8868\u793a\u4e3a\u201cX\u201d\uff09\u4e4b\u95f4\u5b58\u5728\u7ebf\u6027\u5173\u7cfb\u3002\u7ebf\u6027\u5173\u7cfb\u53ef\u4ee5\u8868\u793a\u5982\u4e0b\uff1a<\/p>\n<p>Y = \u03b20 + \u03b21<em>X1+\u03b22<\/em>X2 + \u2026 + \u03b2n*Xn + \u03b5<\/p>\n<p>\u5728\u54ea\u91cc\uff1a<\/p>\n<ul>\n<li>Y \u662f\u56e0\u53d8\u91cf<\/li>\n<li>X1, X2, \u2026, Xn \u662f\u81ea\u53d8\u91cf<\/li>\n<li>\u03b20, \u03b21, \u03b22, \u2026, \u03b2n \u662f\u56de\u5f52\u65b9\u7a0b\u7684\u7cfb\u6570\uff08\u659c\u7387\uff09<\/li>\n<li>\u03b5 \u8868\u793a\u8bef\u5dee\u9879\u6216\u6b8b\u5dee\uff0c\u8bf4\u660e\u6a21\u578b\u672a\u89e3\u91ca\u7684\u53d8\u5f02\u6027<\/li>\n<\/ul>\n<p>\u7ebf\u6027\u56de\u5f52\u7684\u4e3b\u8981\u76ee\u7684\u662f\u786e\u5b9a\u6700\u5c0f\u5316\u6b8b\u5dee\u5e73\u65b9\u548c\u7684\u7cfb\u6570 (\u03b20\u3001\u03b21\u3001\u03b22\u3001\u2026\u3001\u03b2n) \u7684\u503c\uff0c\u4ece\u800c\u63d0\u4f9b\u901a\u8fc7\u6570\u636e\u7684\u6700\u4f73\u62df\u5408\u7ebf\u3002<\/p>\n<h2>\u7ebf\u6027\u56de\u5f52\u7684\u5185\u90e8\u7ed3\u6784\uff1a\u5b83\u662f\u5982\u4f55\u5de5\u4f5c\u7684<\/h2>\n<p>\u7ebf\u6027\u56de\u5f52\u4f7f\u7528\u6570\u5b66\u4f18\u5316\u6280\u672f\uff08\u901a\u5e38\u79f0\u4e3a\u6700\u5c0f\u4e8c\u4e58\u6cd5\uff09\u6765\u4f30\u8ba1\u56de\u5f52\u65b9\u7a0b\u7684\u7cfb\u6570\u3002\u8be5\u8fc7\u7a0b\u6d89\u53ca\u627e\u5230\u4e00\u6761\u76f4\u7ebf\uff0c\u4f7f\u89c2\u5bdf\u5230\u7684\u56e0\u53d8\u91cf\u503c\u4e0e\u4ece\u56de\u5f52\u65b9\u7a0b\u83b7\u5f97\u7684\u9884\u6d4b\u503c\u4e4b\u95f4\u7684\u5e73\u65b9\u5dee\u4e4b\u548c\u6700\u5c0f\u5316\u3002<\/p>\n<p>\u6267\u884c\u7ebf\u6027\u56de\u5f52\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<ol>\n<li>\u6570\u636e\u6536\u96c6\uff1a\u6536\u96c6\u5305\u542b\u56e0\u53d8\u91cf\u548c\u81ea\u53d8\u91cf\u7684\u6570\u636e\u96c6\u3002<\/li>\n<li>\u6570\u636e\u9884\u5904\u7406\uff1a\u6e05\u7406\u6570\u636e\u3001\u5904\u7406\u7f3a\u5931\u503c\u5e76\u6267\u884c\u4efb\u4f55\u5fc5\u8981\u7684\u8f6c\u6362\u3002<\/li>\n<li>\u6a21\u578b\u6784\u5efa\uff1a\u9009\u62e9\u9002\u5f53\u7684\u81ea\u53d8\u91cf\u5e76\u5e94\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u6765\u4f30\u8ba1\u7cfb\u6570\u3002<\/li>\n<li>\u6a21\u578b\u8bc4\u4f30\uff1a\u901a\u8fc7\u5206\u6790\u6b8b\u5dee\u3001R \u5e73\u65b9\u503c\u548c\u5176\u4ed6\u7edf\u8ba1\u6307\u6807\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u62df\u5408\u4f18\u5ea6\u3002<\/li>\n<li>\u9884\u6d4b\uff1a\u4f7f\u7528\u7ecf\u8fc7\u8bad\u7ec3\u7684\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u70b9\u8fdb\u884c\u9884\u6d4b\u3002<\/li>\n<\/ol>\n<h2>\u7ebf\u6027\u56de\u5f52\u7684\u5173\u952e\u7279\u5f81\u5206\u6790<\/h2>\n<p>\u7ebf\u6027\u56de\u5f52\u63d0\u4f9b\u4e86\u51e0\u4e2a\u5173\u952e\u7279\u6027\uff0c\u4f7f\u5176\u6210\u4e3a\u4e00\u79cd\u591a\u529f\u80fd\u4e14\u5e7f\u6cdb\u4f7f\u7528\u7684\u5efa\u6a21\u6280\u672f\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u53ef\u89e3\u91ca\u6027<\/strong>\uff1a\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u7cfb\u6570\u4e3a\u56e0\u53d8\u91cf\u548c\u81ea\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u63d0\u4f9b\u4e86\u6709\u4ef7\u503c\u7684\u89c1\u89e3\u3002\u6bcf\u4e2a\u7cfb\u6570\u7684\u7b26\u53f7\u548c\u5927\u5c0f\u8868\u793a\u5bf9\u56e0\u53d8\u91cf\u5f71\u54cd\u7684\u65b9\u5411\u548c\u5f3a\u5ea6\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6613\u4e8e\u5b9e\u65bd<\/strong>\uff1a\u7ebf\u6027\u56de\u5f52\u76f8\u5bf9\u5bb9\u6613\u7406\u89e3\u548c\u5b9e\u73b0\uff0c\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u5206\u6790\u521d\u5b66\u8005\u548c\u4e13\u5bb6\u7684\u53ef\u884c\u9009\u62e9\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u529f\u80fd\u6027<\/strong>\uff1a\u5c3d\u7ba1\u7ebf\u6027\u56de\u5f52\u5f88\u7b80\u5355\uff0c\u4f46\u5b83\u53ef\u4ee5\u5904\u7406\u5404\u79cd\u7c7b\u578b\u7684\u95ee\u9898\uff0c\u4ece\u7b80\u5355\u7684\u4e00\u53d8\u91cf\u5173\u7cfb\u5230\u66f4\u590d\u6742\u7684\u591a\u5143\u56de\u5f52\u573a\u666f\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u9884\u8a00<\/strong>\uff1a\u4e00\u65e6\u5bf9\u6570\u636e\u8fdb\u884c\u4e86\u6a21\u578b\u8bad\u7ec3\uff0c\u7ebf\u6027\u56de\u5f52\u5c31\u53ef\u4ee5\u7528\u4e8e\u9884\u6d4b\u4efb\u52a1\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5047\u8bbe<\/strong>\uff1a\u7ebf\u6027\u56de\u5f52\u4f9d\u8d56\u4e8e\u51e0\u4e2a\u5047\u8bbe\uff0c\u5305\u62ec\u7ebf\u6027\u3001\u8bef\u5dee\u72ec\u7acb\u6027\u548c\u5e38\u6570\u65b9\u5dee\u7b49\u3002\u8fdd\u53cd\u8fd9\u4e9b\u5047\u8bbe\u53ef\u80fd\u4f1a\u5f71\u54cd\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u7ebf\u6027\u56de\u5f52\u7684\u7c7b\u578b<\/h2>\n<p>\u7ebf\u6027\u56de\u5f52\u6709\u591a\u79cd\u53d8\u4f53\uff0c\u6bcf\u79cd\u53d8\u4f53\u90fd\u65e8\u5728\u89e3\u51b3\u7279\u5b9a\u573a\u666f\u548c\u6570\u636e\u7c7b\u578b\u3002\u4e00\u4e9b\u5e38\u89c1\u7684\u7c7b\u578b\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/strong>\uff1a\u6d89\u53ca\u4e00\u4e2a\u81ea\u53d8\u91cf\u548c\u4e00\u4e2a\u56e0\u53d8\u91cf\uff0c\u4f7f\u7528\u76f4\u7ebf\u5efa\u6a21\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/strong>\uff1a\u5408\u5e76\u4e24\u4e2a\u6216\u591a\u4e2a\u81ea\u53d8\u91cf\u6765\u9884\u6d4b\u56e0\u53d8\u91cf\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u9879\u5f0f\u56de\u5f52<\/strong>\uff1a\u901a\u8fc7\u4f7f\u7528\u9ad8\u9636\u591a\u9879\u5f0f\u9879\u6765\u6355\u83b7\u975e\u7ebf\u6027\u5173\u7cfb\u6765\u6269\u5c55\u7ebf\u6027\u56de\u5f52\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5cad\u56de\u5f52\uff08L2 \u6b63\u5219\u5316\uff09<\/strong>\uff1a\u901a\u8fc7\u5728\u6b8b\u5dee\u5e73\u65b9\u548c\u4e2d\u6dfb\u52a0\u60e9\u7f5a\u9879\u6765\u5f15\u5165\u6b63\u5219\u5316\uff0c\u4ee5\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\u3002<\/p>\n<\/li>\n<li>\n<p><strong>Lasso \u56de\u5f52\uff08L1 \u6b63\u5219\u5316\uff09<\/strong>\uff1a\u53e6\u4e00\u79cd\u6b63\u5219\u5316\u6280\u672f\uff0c\u53ef\u4ee5\u901a\u8fc7\u5c06\u4e00\u4e9b\u56de\u5f52\u7cfb\u6570\u9a71\u52a8\u5230\u6070\u597d\u4e3a\u96f6\u6765\u6267\u884c\u7279\u5f81\u9009\u62e9\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5f39\u6027\u7f51\u7edc\u56de\u5f52<\/strong>\uff1a\u7ed3\u5408\u4e86 L1 \u548c L2 \u6b63\u5219\u5316\u65b9\u6cd5\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u867d\u7136\u540d\u79f0\u4e2d\u5305\u542b\u201c\u56de\u5f52\u201d\uff0c\u4f46\u5b83\u7528\u4e8e\u4e8c\u5143\u5206\u7c7b\u95ee\u9898\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u4e0b\u9762\u7684\u8868\u683c\u603b\u7ed3\u4e86\u7ebf\u6027\u56de\u5f52\u7684\u7c7b\u578b\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u7c7b\u578b<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/td>\n<td>\u4e00\u4e2a\u56e0\u53d8\u91cf\u548c\u4e00\u4e2a\u81ea\u53d8\u91cf<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/td>\n<td>\u591a\u4e2a\u81ea\u53d8\u91cf\u548c\u4e00\u4e2a\u56e0\u53d8\u91cf<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u9879\u5f0f\u56de\u5f52<\/td>\n<td>\u975e\u7ebf\u6027\u5173\u7cfb\u7684\u9ad8\u9636\u591a\u9879\u5f0f\u9879<\/td>\n<\/tr>\n<tr>\n<td>\u5cad\u56de\u5f52<\/td>\n<td>L2\u6b63\u5219\u5316\u9632\u6b62\u8fc7\u62df\u5408<\/td>\n<\/tr>\n<tr>\n<td>\u5957\u7d22\u56de\u5f52<\/td>\n<td>\u5e26\u7279\u5f81\u9009\u62e9\u7684 L1 \u6b63\u5219\u5316<\/td>\n<\/tr>\n<tr>\n<td>\u5f39\u6027\u7f51\u7edc\u56de\u5f52<\/td>\n<td>\u7ed3\u5408 L1 \u548c L2 \u6b63\u5219\u5316<\/td>\n<\/tr>\n<tr>\n<td>\u903b\u8f91\u56de\u5f52<\/td>\n<td>\u4e8c\u5143\u5206\u7c7b\u95ee\u9898<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u7ebf\u6027\u56de\u5f52\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u4f7f\u7528\u76f8\u5173\u7684\u89e3\u51b3\u65b9\u6848<\/h2>\n<p>\u7ebf\u6027\u56de\u5f52\u5728\u7814\u7a76\u548c\u5b9e\u9645\u73af\u5883\u4e2d\u90fd\u6709\u591a\u79cd\u5e94\u7528\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u7ecf\u6d4e\u5206\u6790<\/strong>\uff1a\u7528\u4e8e\u5206\u6790\u7ecf\u6d4e\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u4f8b\u5982GDP\u548c\u5931\u4e1a\u7387\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u9500\u552e\u548c\u8425\u9500<\/strong>\uff1a\u7ebf\u6027\u56de\u5f52\u6709\u52a9\u4e8e\u6839\u636e\u8425\u9500\u652f\u51fa\u548c\u5176\u4ed6\u56e0\u7d20\u9884\u6d4b\u9500\u552e\u989d\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8d22\u52a1\u9884\u6d4b<\/strong>\uff1a\u7528\u4e8e\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u3001\u8d44\u4ea7\u4ef7\u503c\u548c\u5176\u4ed6\u8d22\u52a1\u6307\u6807\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u536b\u751f\u4fdd\u5065<\/strong>\uff1a\u7ebf\u6027\u56de\u5f52\u7528\u4e8e\u7814\u7a76\u81ea\u53d8\u91cf\u5bf9\u5065\u5eb7\u7ed3\u679c\u7684\u5f71\u54cd\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5929\u6c14\u9884\u62a5<\/strong>\uff1a\u5b83\u7528\u4e8e\u6839\u636e\u5386\u53f2\u6570\u636e\u9884\u6d4b\u5929\u6c14\u6a21\u5f0f\u3002<\/p>\n<\/li>\n<\/ol>\n<h3>\u6311\u6218\u548c\u89e3\u51b3\u65b9\u6848\uff1a<\/h3>\n<ul>\n<li>\n<p><strong>\u8fc7\u62df\u5408<\/strong>\uff1a\u5982\u679c\u6a21\u578b\u76f8\u5bf9\u4e8e\u6570\u636e\u8fc7\u4e8e\u590d\u6742\uff0c\u7ebf\u6027\u56de\u5f52\u53ef\u80fd\u4f1a\u51fa\u73b0\u8fc7\u5ea6\u62df\u5408\u3002 Ridge \u548c Lasso 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IP 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href=\"https:\/\/en.wikipedia.org\/wiki\/Linear_regression\" target=\"_new\" rel=\"noopener nofollow\">\u7ef4\u57fa\u767e\u79d1 \u2013 \u7ebf\u6027\u56de\u5f52<\/a><\/li>\n<li><a href=\"https:\/\/web.stanford.edu\/~hastie\/ElemStatLearn\/\" target=\"_new\" rel=\"noopener nofollow\">\u7edf\u8ba1\u5b66\u4e60\u2014\u2014\u7ebf\u6027\u56de\u5f52<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/linear_model.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn \u6587\u6863 \u2013 \u7ebf\u6027\u56de\u5f52<\/a><\/li>\n<li><a href=\"https:\/\/www.coursera.org\/learn\/machine-learning\" target=\"_new\" rel=\"noopener nofollow\">Coursera \u2013 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Overview<\/mark>","faq_items":[{"question":"What is Linear regression?","answer":"<p>Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It aims to find a linear equation that best fits the data, allowing for predictions and insights into underlying patterns.<\/p>"},{"question":"Who first developed Linear regression?","answer":"<p>The method of least squares, a foundational part of linear regression, was independently used by Carl Friedrich Gauss and Adrien-Marie Legendre in the early 19th century, both in the field of astronomy.<\/p>"},{"question":"How does Linear regression work?","answer":"<p>Linear regression estimates the coefficients of the regression equation through the method of least squares, minimizing the sum of squared differences between observed and predicted values. It then provides a linear equation that represents the best-fitting line through the data.<\/p>"},{"question":"What are the types of Linear regression?","answer":"<p>There are various types of linear regression, including Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, and Logistic Regression for binary classification.<\/p>"},{"question":"What are the main characteristics of Linear regression?","answer":"<p>Linear regression offers interpretability, ease of implementation, versatility, and the ability to make predictions. However, it assumes certain assumptions like linearity, independence of errors, and constant variance.<\/p>"},{"question":"How can Linear regression be used?","answer":"<p>Linear regression finds applications in economic analysis, sales, marketing, finance, healthcare, and weather prediction, among others. It helps in predicting outcomes, analyzing relationships, and making informed decisions.<\/p>"},{"question":"What challenges can arise in Linear regression?","answer":"<p>Challenges in linear regression include overfitting, multicollinearity (high correlation between variables), and handling nonlinearity in data. Regularization techniques can be used to address these challenges.<\/p>"},{"question":"How does Linear regression relate to proxy servers?","answer":"<p>Proxy servers enhance data privacy and security by acting as intermediaries between users and the internet. When combined with linear regression, they can anonymize data, access geographically restricted datasets, and perform location-based regression.<\/p>"},{"question":"What are the future perspectives of Linear regression?","answer":"<p>As technology advances, linear regression is expected to benefit from automation, machine learning integration, and further developments in regularization techniques. Its interdisciplinary applications will continue to expand.<\/p>"},{"question":"Where can I find more information about Linear regression?","answer":"<p>For more detailed information on linear regression, you can explore resources like Wikipedia, Stanford's Statistical Learning materials, Scikit-learn documentation, and Coursera's Machine Learning with Andrew Ng course. OneProxy is your reliable source for all your linear regression needs!<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477831","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477831\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468779"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=477831"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}