{"id":477879,"date":"2023-08-09T09:21:36","date_gmt":"2023-08-09T09:21:36","guid":{"rendered":""},"modified":"2023-09-05T11:15:36","modified_gmt":"2023-09-05T11:15:36","slug":"logistic-regression","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/logistic-regression\/","title":{"rendered":"\u903b\u8f91\u56de\u5f52"},"content":{"rendered":"<p>\u903b\u8f91\u56de\u5f52\u662f\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u5206\u6790\u9886\u57df\u5e7f\u6cdb\u4f7f\u7528\u7684\u7edf\u8ba1\u6280\u672f\u3002\u5b83\u5c5e\u4e8e\u76d1\u7763\u5b66\u4e60\u7684\u8303\u7574\uff0c\u5176\u76ee\u6807\u662f\u6839\u636e\u8f93\u5165\u7279\u5f81\u9884\u6d4b\u5206\u7c7b\u7ed3\u679c\u3002\u4e0e\u9884\u6d4b\u8fde\u7eed\u6570\u503c\u7684\u7ebf\u6027\u56de\u5f52\u4e0d\u540c\uff0c\u903b\u8f91\u56de\u5f52\u9884\u6d4b\u4e8b\u4ef6\u53d1\u751f\u7684\u6982\u7387\uff0c\u901a\u5e38\u662f\u4e8c\u5143\u7ed3\u679c\uff0c\u4f8b\u5982\u662f\/\u5426\u3001\u771f\/\u5047\u6216 0\/1\u3002<\/p>\n<h2>Logistic \u56de\u5f52\u7684\u8d77\u6e90\u5386\u53f2\u53ca\u5176\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>\u903b\u8f91\u56de\u5f52\u7684\u6982\u5ff5\u53ef\u4ee5\u8ffd\u6eaf\u5230 19 \u4e16\u7eaa\u4e2d\u53f6\uff0c\u4f46\u5b83\u5728 20 \u4e16\u7eaa\u56e0\u7edf\u8ba1\u5b66\u5bb6 David Cox \u7684\u5de5\u4f5c\u800c\u58f0\u540d\u9e4a\u8d77\u3002\u4ed6\u901a\u5e38\u88ab\u8ba4\u4e3a\u662f 1958 \u5e74\u5f00\u53d1\u903b\u8f91\u56de\u5f52\u6a21\u578b\u7684\u4eba\uff0c\u540e\u6765\u8be5\u6a21\u578b\u88ab\u5176\u4ed6\u7edf\u8ba1\u5b66\u5bb6\u548c\u7814\u7a76\u4eba\u5458\u63a8\u5e7f\u3002<\/p>\n<h2>\u6709\u5173\u903b\u8f91\u56de\u5f52\u7684\u8be6\u7ec6\u4fe1\u606f<\/h2>\n<p>\u903b\u8f91\u56de\u5f52\u4e3b\u8981\u7528\u4e8e\u4e8c\u5143\u5206\u7c7b\u95ee\u9898\uff0c\u5176\u4e2d\u54cd\u5e94\u53d8\u91cf\u53ea\u6709\u4e24\u79cd\u53ef\u80fd\u7684\u7ed3\u679c\u3002\u8be5\u6280\u672f\u5229\u7528\u903b\u8f91\u51fd\u6570\uff08\u4e5f\u79f0\u4e3a S \u578b\u51fd\u6570\uff09\u5c06\u8f93\u5165\u7279\u5f81\u6620\u5c04\u5230\u6982\u7387\u3002<\/p>\n<p>\u903b\u8f91\u51fd\u6570\u5b9a\u4e49\u4e3a\uff1a<\/p>\n<p><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>\u78f7<\/mi><mo stretchy=\"false\">(<\/mo><mi>y<\/mi><mo>=<\/mo><mn>1<\/mn><mo stretchy=\"false\">)<\/mo><mo>=<\/mo><mfrac><mn>1<\/mn><mrow><mn>1<\/mn><mo>+<\/mo><msup><mi>e<\/mi><mrow><mo>\u2212<\/mo><mi>\u662f<\/mi><\/mrow><\/msup><\/mrow><\/mfrac><\/mrow><annotation encoding=\"application\/x-tex\">P(y=1) = frac{1}{1 + e^{ -z}}<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em; vertical-align: -0.25em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.13889em;\">\u78f7<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">y<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><span class=\"mrel\">=<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em; vertical-align: -0.25em;\"><\/span><span class=\"mord\">1<\/span><span class=\"mclose\">)<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><span class=\"mrel\">=<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.2484em; vertical-align: -0.4033em;\"><\/span><span class=\"mord\"><span class=\"mopen nulldelimiter\"><\/span><span class=\"mfrac\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8451em;\"><span style=\"top: -2.655em;\"><span class=\"pstrut\" style=\"height: 3em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mtight\">1<\/span><span class=\"mbin mtight\">+<\/span><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">e<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.7027em;\"><span style=\"top: -2.786em; margin-right: 0.0714em;\"><span class=\"pstrut\" style=\"height: 2.5em;\"><\/span><span class=\"sizing reset-size3 size1 mtight\"><span class=\"mord mtight\"><span class=\"mord mtight\">\u2212<\/span><span class=\"mord mathnormal mtight\" style=\"margin-right: 0.04398em;\">\u662f<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span style=\"top: -3.23em;\"><span class=\"pstrut\" style=\"height: 3em;\"><\/span><span class=\"frac-line\" style=\"border-bottom-width: 0.04em;\"><\/span><\/span><span style=\"top: -3.394em;\"><span class=\"pstrut\" style=\"height: 3em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mtight\">1<\/span><\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.4033em;\"><span><\/span><\/span><\/span><\/span><\/span><span class=\"mclose nulldelimiter\"><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<p>\u5728\u54ea\u91cc\uff1a<\/p>\n<ul>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>\u78f7<\/mi><mo stretchy=\"false\">(<\/mo><mi>y<\/mi><mo>=<\/mo><mn>1<\/mn><mo stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">P(y=1)<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em; vertical-align: -0.25em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.13889em;\">\u78f7<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">y<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><span class=\"mrel\">=<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em; vertical-align: -0.25em;\"><\/span><span class=\"mord\">1<\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span> \u8868\u793a\u6b63\u7c7b\uff08\u7ed3\u679c 1\uff09\u7684\u6982\u7387\u3002<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>\u662f<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\u662f<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.04398em;\">\u662f<\/span><\/span><\/span><\/span><\/span> \u662f\u8f93\u5165\u7279\u5f81\u53ca\u5176\u5bf9\u5e94\u6743\u91cd\u7684\u7ebf\u6027\u7ec4\u5408\u3002<\/li>\n<\/ul>\n<p>\u903b\u8f91\u56de\u5f52\u6a21\u578b\u8bd5\u56fe\u627e\u5230\u5c06\u4e24\u4e2a\u7c7b\u522b\u5206\u5f00\u7684\u6700\u4f73\u62df\u5408\u7ebf\uff08\u6216\u66f4\u9ad8\u7ef4\u5ea6\u4e2d\u7684\u8d85\u5e73\u9762\uff09\u3002\u8be5\u7b97\u6cd5\u4f7f\u7528\u5404\u79cd\u4f18\u5316\u6280\u672f\uff08\u4f8b\u5982\u68af\u5ea6\u4e0b\u964d\uff09\u4f18\u5316\u6a21\u578b\u53c2\u6570\uff0c\u4ee5\u6700\u5927\u9650\u5ea6\u5730\u51cf\u5c11\u9884\u6d4b\u6982\u7387\u548c\u5b9e\u9645\u7c7b\u522b\u6807\u7b7e\u4e4b\u95f4\u7684\u8bef\u5dee\u3002<\/p>\n<h2>Logistic \u56de\u5f52\u7684\u5185\u90e8\u7ed3\u6784\uff1aLogistic \u56de\u5f52\u7684\u5de5\u4f5c\u539f\u7406<\/h2>\n<p>\u903b\u8f91\u56de\u5f52\u7684\u5185\u90e8\u7ed3\u6784\u6d89\u53ca\u4ee5\u4e0b\u5173\u952e\u7ec4\u6210\u90e8\u5206\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u8f93\u5165\u7279\u5f81<\/strong>\uff1a\u8fd9\u4e9b\u662f\u4f5c\u4e3a\u76ee\u6807\u53d8\u91cf\u7684\u9884\u6d4b\u56e0\u5b50\u7684\u53d8\u91cf\u6216\u5c5e\u6027\u3002\u6bcf\u4e2a\u8f93\u5165\u7279\u5f81\u90fd\u88ab\u5206\u914d\u4e00\u4e2a\u6743\u91cd\uff0c\u8be5\u6743\u91cd\u51b3\u5b9a\u4e86\u5176\u5bf9\u9884\u6d4b\u6982\u7387\u7684\u5f71\u54cd\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u91cd\u91cf<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u4e3a\u6bcf\u4e2a\u8f93\u5165\u7279\u5f81\u5206\u914d\u4e00\u4e2a\u6743\u91cd\uff0c\u8868\u793a\u5176\u5bf9\u6574\u4f53\u9884\u6d4b\u7684\u8d21\u732e\u3002\u6b63\u6743\u91cd\u8868\u793a\u4e0e\u6b63\u7c7b\u6b63\u76f8\u5173\uff0c\u800c\u8d1f\u6743\u91cd\u8868\u793a\u8d1f\u76f8\u5173\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u504f\u5dee\uff08\u622a\u8ddd\uff09<\/strong>\uff1a\u504f\u5dee\u9879\u88ab\u6dfb\u52a0\u5230\u8f93\u5165\u7279\u5f81\u7684\u52a0\u6743\u548c\u4e2d\u3002\u5b83\u5145\u5f53\u504f\u79fb\u91cf\uff0c\u4f7f\u6a21\u578b\u80fd\u591f\u6355\u83b7\u6b63\u7c7b\u7684\u57fa\u7ebf\u6982\u7387\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u903b\u8f91\u51fd\u6570<\/strong>\uff1a\u903b\u8f91\u51fd\u6570\uff0c\u524d\u9762\u63d0\u5230\u8fc7\uff0c\u5c06\u8f93\u5165\u7279\u5f81\u4e0e\u504f\u5dee\u9879\u7684\u52a0\u6743\u548c\u6620\u5c04\u4e3a0\u52301\u4e4b\u95f4\u7684\u6982\u7387\u503c\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u51b3\u7b56\u8fb9\u754c<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u6a21\u578b\u4f7f\u7528\u51b3\u7b56\u8fb9\u754c\u5c06\u4e24\u4e2a\u7c7b\u522b\u5206\u5f00\u3002\u51b3\u7b56\u8fb9\u754c\u662f\u4e00\u4e2a\u9608\u503c\u6982\u7387\u503c\uff08\u901a\u5e38\u4e3a 0.5\uff09\uff0c\u9ad8\u4e8e\u8be5\u9608\u503c\u7684\u8f93\u5165\u88ab\u5f52\u7c7b\u4e3a\u6b63\u7c7b\uff0c\u4f4e\u4e8e\u8be5\u9608\u503c\u7684\u8f93\u5165\u88ab\u5f52\u7c7b\u4e3a\u8d1f\u7c7b\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>Logistic\u56de\u5f52\u7684\u5173\u952e\u7279\u5f81\u5206\u6790<\/h2>\n<p>\u903b\u8f91\u56de\u5f52\u6709\u51e0\u4e2a\u57fa\u672c\u7279\u5f81\uff0c\u4f7f\u5176\u6210\u4e3a\u4e8c\u5143\u5206\u7c7b\u4efb\u52a1\u7684\u70ed\u95e8\u9009\u62e9\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u7b80\u5355\u4e14\u6613\u4e8e\u89e3\u91ca<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u7684\u5b9e\u73b0\u548c\u89e3\u91ca\u76f8\u5bf9\u7b80\u5355\u3002\u6a21\u578b\u7684\u6743\u91cd\u53ef\u4ee5\u6d1e\u6089\u6bcf\u4e2a\u7279\u5f81\u5728\u9884\u6d4b\u7ed3\u679c\u4e2d\u7684\u91cd\u8981\u6027\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6982\u7387\u8f93\u51fa<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u4e0d\u63d0\u4f9b\u79bb\u6563\u5206\u7c7b\uff0c\u800c\u662f\u63d0\u4f9b\u5c5e\u4e8e\u7279\u5b9a\u7c7b\u522b\u7684\u6982\u7387\uff0c\u8fd9\u5728\u51b3\u7b56\u8fc7\u7a0b\u4e2d\u5f88\u6709\u7528\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u53ef\u6269\u5c55\u6027<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u53ef\u4ee5\u6709\u6548\u5730\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\uff0c\u4f7f\u5176\u9002\u7528\u4e8e\u5404\u79cd\u5e94\u7528\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5bf9\u5f02\u5e38\u503c\u5177\u6709\u9c81\u68d2\u6027<\/strong>\uff1a\u4e0e\u652f\u6301\u5411\u91cf\u673a\u7b49\u5176\u4ed6\u7b97\u6cd5\u76f8\u6bd4\uff0c\u903b\u8f91\u56de\u5f52\u5bf9\u5f02\u5e38\u503c\u7684\u654f\u611f\u5ea6\u8f83\u4f4e\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u903b\u8f91\u56de\u5f52\u7684\u7c7b\u578b<\/h2>\n<p>\u903b\u8f91\u56de\u5f52\u6709\u51e0\u79cd\u53d8\u4f53\uff0c\u6bcf\u79cd\u53d8\u4f53\u90fd\u9488\u5bf9\u7279\u5b9a\u573a\u666f\u91cf\u8eab\u5b9a\u5236\u3002\u903b\u8f91\u56de\u5f52\u7684\u4e3b\u8981\u7c7b\u578b\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u4e8c\u5143\u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u4e8c\u5143\u5206\u7c7b\u903b\u8f91\u56de\u5f52\u7684\u6807\u51c6\u5f62\u5f0f\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u9879\u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u5f53\u6709\u4e24\u4e2a\u4ee5\u4e0a\u7684\u6392\u4ed6\u6027\u7c7b\u522b\u9700\u8981\u9884\u6d4b\u65f6\u4f7f\u7528\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6709\u5e8f\u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u9002\u7528\u4e8e\u9884\u6d4b\u5177\u6709\u81ea\u7136\u6392\u5e8f\u7684\u5e8f\u6570\u7c7b\u522b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6b63\u5219\u5316\u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u5f15\u5165\u6b63\u5219\u5316\u6280\u672f\uff0c\u5982 L1\uff08Lasso\uff09\u6216 L2\uff08Ridge\uff09\u6b63\u5219\u5316\uff0c\u4ee5\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u4e0b\u8868\u603b\u7ed3\u4e86\u903b\u8f91\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>\u4e8c\u5143\u903b\u8f91\u56de\u5f52<\/td>\n<td>\u4e8c\u5143\u7ed3\u679c\u7684\u6807\u51c6\u903b\u8f91\u56de\u5f52<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u9879\u903b\u8f91\u56de\u5f52<\/td>\n<td>\u5bf9\u4e8e\u591a\u4e2a\u4e13\u5c5e\u7c7b<\/td>\n<\/tr>\n<tr>\n<td>\u6709\u5e8f\u903b\u8f91\u56de\u5f52<\/td>\n<td>\u5bf9\u4e8e\u5177\u6709\u81ea\u7136\u987a\u5e8f\u7684\u5e8f\u6570\u7c7b\u522b<\/td>\n<\/tr>\n<tr>\n<td>\u6b63\u5219\u5316\u903b\u8f91\u56de\u5f52<\/td>\n<td>\u5f15\u5165\u6b63\u5219\u5316\u4ee5\u9632\u6b62\u8fc7\u5ea6\u62df\u5408<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Logistic \u56de\u5f52\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6cd5<\/h2>\n<p>\u903b\u8f91\u56de\u5f52\u7531\u4e8e\u5176\u591a\u529f\u80fd\u6027\u800c\u5e94\u7528\u4e8e\u5404\u4e2a\u9886\u57df\u3002\u4e00\u4e9b\u5e38\u89c1\u7684\u7528\u4f8b\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u533b\u7597\u8bca\u65ad<\/strong>\uff1a\u6839\u636e\u60a3\u8005\u75c7\u72b6\u548c\u6d4b\u8bd5\u7ed3\u679c\u9884\u6d4b\u75be\u75c5\u7684\u5b58\u5728\u6216\u4e0d\u5b58\u5728\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4fe1\u7528\u98ce\u9669\u8bc4\u4f30<\/strong>\uff1a\u8bc4\u4f30\u8d37\u6b3e\u7533\u8bf7\u4eba\u7684\u8fdd\u7ea6\u98ce\u9669\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5e02\u573a\u8425\u9500\u4e0e\u9500\u552e<\/strong>\uff1a\u8bc6\u522b\u53ef\u80fd\u8fdb\u884c\u8d2d\u4e70\u7684\u6f5c\u5728\u5ba2\u6237\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u60c5\u611f\u5206\u6790<\/strong>\uff1a\u5c06\u6587\u672c\u6570\u636e\u4e2d\u8868\u8fbe\u7684\u89c2\u70b9\u5206\u7c7b\u4e3a\u79ef\u6781\u6216\u6d88\u6781\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u7136\u800c\u903b\u8f91\u56de\u5f52\u4e5f\u5b58\u5728\u4e00\u4e9b\u5c40\u9650\u6027\u548c\u6311\u6218\uff0c\u4f8b\u5982\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u4e0d\u5e73\u8861<\/strong>\uff1a\u5f53\u4e00\u4e2a\u7c7b\u522b\u7684\u6bd4\u4f8b\u660e\u663e\u9ad8\u4e8e\u53e6\u4e00\u4e2a\u7c7b\u522b\u65f6\uff0c\u6a21\u578b\u53ef\u80fd\u4f1a\u504f\u5411\u591a\u6570\u7c7b\u522b\u3002\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u53ef\u80fd\u9700\u8981\u4f7f\u7528\u91cd\u91c7\u6837\u6216\u4f7f\u7528\u7c7b\u522b\u52a0\u6743\u65b9\u6cd5\u7b49\u6280\u672f\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u975e\u7ebf\u6027\u5173\u7cfb<\/strong>\uff1a\u903b\u8f91\u56de\u5f52\u5047\u8bbe\u8f93\u5165\u7279\u5f81\u4e0e\u7ed3\u679c\u5bf9\u6570\u51e0\u7387\u4e4b\u95f4\u5b58\u5728\u7ebf\u6027\u5173\u7cfb\u3002\u5982\u679c\u5173\u7cfb\u662f\u975e\u7ebf\u6027\u7684\uff0c\u51b3\u7b56\u6811\u6216\u795e\u7ecf\u7f51\u7edc\u7b49\u66f4\u590d\u6742\u7684\u6a21\u578b\u53ef\u80fd\u66f4\u5408\u9002\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8fc7\u62df\u5408<\/strong>\uff1a\u5904\u7406\u9ad8\u7ef4\u6570\u636e\u6216\u5927\u91cf\u7279\u5f81\u65f6\uff0c\u903b\u8f91\u56de\u5f52\u5bb9\u6613\u51fa\u73b0\u8fc7\u5ea6\u62df\u5408\u3002\u6b63\u5219\u5316\u6280\u672f\u53ef\u4ee5\u5e2e\u52a9\u7f13\u89e3\u6b64\u95ee\u9898\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u4e0e\u540c\u7c7b\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83<\/h2>\n<p>\u8ba9\u6211\u4eec\u5c06\u903b\u8f91\u56de\u5f52\u4e0e\u5176\u4ed6\u7c7b\u4f3c\u6280\u672f\u8fdb\u884c\u6bd4\u8f83\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u6280\u672f<\/th>\n<th>\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u7ebf\u6027\u56de\u5f52<\/td>\n<td>\u7528\u4e8e\u9884\u6d4b\u8fde\u7eed\u6570\u503c\uff0c\u800c\u903b\u8f91\u56de\u5f52\u5219\u9884\u6d4b\u4e8c\u5143\u7ed3\u679c\u7684\u6982\u7387\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u652f\u6301\u5411\u91cf\u673a<\/td>\n<td>\u9002\u7528\u4e8e\u4e8c\u5143\u5206\u7c7b\u548c\u591a\u7c7b\u5206\u7c7b\uff0c\u800c\u903b\u8f91\u56de\u5f52\u4e3b\u8981\u7528\u4e8e\u4e8c\u5143\u5206\u7c7b\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u51b3\u7b56\u6811<\/td>\n<td>\u975e\u53c2\u6570\u53ef\u4ee5\u6355\u6349\u975e\u7ebf\u6027\u5173\u7cfb\uff0c\u800c\u903b\u8f91\u56de\u5f52\u5047\u8bbe\u7ebf\u6027\u5173\u7cfb\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u795e\u7ecf\u7f51\u7edc<\/td>\n<td>\u5bf9\u4e8e\u590d\u6742\u4efb\u52a1\u5177\u6709\u9ad8\u5ea6\u7075\u6d3b\u6027\uff0c\u4f46\u5b83\u4eec\u6bd4\u903b\u8f91\u56de\u5f52\u9700\u8981\u66f4\u591a\u7684\u6570\u636e\u548c\u8ba1\u7b97\u8d44\u6e90\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u903b\u8f91\u56de\u5f52\u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f<\/h2>\n<p>\u968f\u7740\u6280\u672f\u7684\u4e0d\u65ad\u8fdb\u6b65\uff0c\u903b\u8f91\u56de\u5f52\u4ecd\u5c06\u662f\u4e8c\u5143\u5206\u7c7b\u4efb\u52a1\u7684\u57fa\u672c\u5de5\u5177\u3002\u7136\u800c\uff0c\u903b\u8f91\u56de\u5f52\u7684\u672a\u6765\u5728\u4e8e\u5b83\u4e0e\u5176\u4ed6\u5c16\u7aef\u6280\u672f\u7684\u878d\u5408\uff0c\u4f8b\u5982\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u96c6\u6210\u65b9\u6cd5<\/strong>\uff1a\u7ed3\u5408\u591a\u4e2a\u903b\u8f91\u56de\u5f52\u6a21\u578b\u6216\u4f7f\u7528\u968f\u673a\u68ee\u6797\u548c\u68af\u5ea6\u63d0\u5347\u7b49\u96c6\u6210\u6280\u672f\u53ef\u4ee5\u63d0\u9ad8\u9884\u6d4b\u6027\u80fd\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6df1\u5ea6\u5b66\u4e60<\/strong>\uff1a\u5c06\u903b\u8f91\u56de\u5f52\u5c42\u7eb3\u5165\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u53ef\u4ee5\u589e\u5f3a\u53ef\u89e3\u91ca\u6027\u5e76\u5e26\u6765\u66f4\u51c6\u786e\u7684\u9884\u6d4b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8d1d\u53f6\u65af\u903b\u8f91\u56de\u5f52<\/strong>\uff1a\u91c7\u7528\u8d1d\u53f6\u65af\u65b9\u6cd5\u53ef\u4ee5\u4e3a\u6a21\u578b\u9884\u6d4b\u63d0\u4f9b\u4e0d\u786e\u5b9a\u6027\u4f30\u8ba1\uff0c\u4f7f\u51b3\u7b56\u8fc7\u7a0b\u66f4\u52a0\u53ef\u9760\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e\u903b\u8f91\u56de\u5f52\u5173\u8054<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u5728\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff08\u5305\u62ec\u903b\u8f91\u56de\u5f52\uff09\u7684\u6570\u636e\u6536\u96c6\u548c\u9884\u5904\u7406\u4e2d\u8d77\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\u3002\u4ee5\u4e0b\u662f\u4ee3\u7406\u670d\u52a1\u5668\u4e0e\u903b\u8f91\u56de\u5f52\u5173\u8054\u7684\u4e00\u4e9b\u65b9\u5f0f\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u6293\u53d6<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u7528\u4e8e\u4ece\u7f51\u7edc\u4e0a\u6293\u53d6\u6570\u636e\uff0c\u786e\u4fdd\u533f\u540d\u6027\u5e76\u9632\u6b62 IP \u963b\u6b62\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u9884\u5904\u7406<\/strong>\uff1a\u5728\u5904\u7406\u5730\u7406\u5206\u5e03\u7684\u6570\u636e\u65f6\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u4f7f\u7814\u7a76\u4eba\u5458\u80fd\u591f\u8bbf\u95ee\u548c\u9884\u5904\u7406\u6765\u81ea\u4e0d\u540c\u5730\u533a\u7684\u6570\u636e\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6a21\u578b\u90e8\u7f72\u4e2d\u7684\u533f\u540d\u6027<\/strong>\uff1a\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u9700\u8981\u90e8\u7f72\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5e76\u589e\u52a0\u533f\u540d\u63aa\u65bd\u4ee5\u4fdd\u62a4\u654f\u611f\u4fe1\u606f\u3002\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5145\u5f53\u4e2d\u4ecb\u6765\u4fdd\u62a4\u7528\u6237\u9690\u79c1\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8d1f\u8f7d\u5747\u8861<\/strong>\uff1a\u5bf9\u4e8e\u5927\u578b\u5e94\u7528\u7a0b\u5e8f\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5728\u903b\u8f91\u56de\u5f52\u6a21\u578b\u7684\u591a\u4e2a\u5b9e\u4f8b\u4e4b\u95f4\u5206\u914d\u4f20\u5165\u7684\u8bf7\u6c42\uff0c\u4ece\u800c\u4f18\u5316\u6027\u80fd\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173\u903b\u8f91\u56de\u5f52\u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u63a2\u7d22\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ol>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Logistic_regression\" target=\"_new\" rel=\"noopener nofollow\">\u903b\u8f91\u56de\u5f52\u2014\u2014\u7ef4\u57fa\u767e\u79d1<\/a><\/li>\n<li><a href=\"https:\/\/web.stanford.edu\/class\/archive\/cs\/cs109\/cs109.1166\/stuff\/tutorials\/02-Logistic-Regression.pdf\" target=\"_new\" rel=\"noopener nofollow\">\u903b\u8f91\u56de\u5f52\u7b80\u4ecb\u2014\u2014\u65af\u5766\u798f\u5927\u5b66<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/logistic-regression-for-machine-learning\/\" target=\"_new\" rel=\"noopener nofollow\">\u673a\u5668\u5b66\u4e60\u7684\u903b\u8f91\u56de\u5f52 \u2013 \u673a\u5668\u5b66\u4e60\u7cbe\u901a<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/introduction-to-logistic-regression-66248243c148\" target=\"_new\" rel=\"noopener nofollow\">\u903b\u8f91\u56de\u5f52\u7b80\u4ecb \u2013 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Asked Questions about <mark>Logistic Regression: Unveiling the Power of Predictive Modeling<\/mark>","faq_items":[{"question":"What is logistic regression?","answer":"<p>Logistic regression is a widely used statistical technique in machine learning and data analysis. It is used to predict the probability of binary outcomes, such as yes\/no or true\/false, based on input features.<\/p>"},{"question":"Who developed logistic regression?","answer":"<p>Logistic regression was developed by statistician David Cox in 1958, though the concept dates back to the mid-19th century. It gained popularity through the works of various researchers and statisticians.<\/p>"},{"question":"How does logistic regression work?","answer":"<p>Logistic regression works by using a logistic function (sigmoid function) to map input features to probabilities. It assigns weights to each input feature and calculates a linear combination of these features. The logistic function converts this linear combination into a probability value between 0 and 1.<\/p>"},{"question":"What are the key features of logistic regression?","answer":"<p>Logistic regression is simple, interpretable, and provides probabilistic output. It is suitable for binary classification tasks and can handle large datasets efficiently. Moreover, it is robust to outliers compared to some other algorithms.<\/p>"},{"question":"What are the types of logistic regression?","answer":"<p>There are several types of logistic regression:<\/p><ol><li>Binary Logistic Regression: For binary outcomes.<\/li><li>Multinomial Logistic Regression: For multiple exclusive classes.<\/li><li>Ordinal Logistic Regression: For ordinal categories with a natural ordering.<\/li><li>Regularized Logistic Regression: Introduces regularization to prevent overfitting.<\/li><\/ol>"},{"question":"Where can logistic regression be used?","answer":"<p>Logistic regression finds applications in various fields, such as medical diagnosis, credit risk assessment, marketing, and sentiment analysis.<\/p>"},{"question":"What are the challenges related to using logistic regression?","answer":"<p>Some challenges with logistic regression include:<\/p><ol><li>Imbalanced data, where one class is much more frequent than the other.<\/li><li>Non-linear relationships between input features and outcomes.<\/li><li>Overfitting with high-dimensional data.<\/li><\/ol>"},{"question":"How can proxy servers be associated with logistic regression?","answer":"<p>Proxy servers can assist logistic regression in data scraping, data preprocessing, anonymizing model deployment, and load balancing in large-scale applications. They play a crucial role in secure and efficient data processing for logistic regression and other machine learning tasks.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477879","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\/477879\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468806"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=477879"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}