{"id":477558,"date":"2023-08-09T09:16:45","date_gmt":"2023-08-09T09:16:45","guid":{"rendered":""},"modified":"2023-09-05T11:14:58","modified_gmt":"2023-09-05T11:14:58","slug":"imbalanced-data","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/imbalanced-data\/","title":{"rendered":"\u6570\u636e\u4e0d\u5e73\u8861"},"content":{"rendered":"<p>\u4e0d\u5e73\u8861\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u4e00\u4e2a\u5e38\u89c1\u6311\u6218\uff0c\u6570\u636e\u96c6\u5185\u7684\u7c7b\u522b\u5206\u5e03\u9ad8\u5ea6\u4e0d\u5e73\u8861\u3002\u8fd9\u610f\u5473\u7740\u4e00\u4e2a\u7c7b\u522b\uff08\u5c11\u6570\u7c7b\u522b\uff09\u4e0e\u53e6\u4e00\u4e2a\u7c7b\u522b\uff08\u591a\u6570\u7c7b\u522b\uff09\u76f8\u6bd4\u4ee3\u8868\u6027\u660e\u663e\u4e0d\u8db3\u3002\u6570\u636e\u4e0d\u5e73\u8861\u95ee\u9898\u4f1a\u5bf9\u5404\u79cd\u6570\u636e\u9a71\u52a8\u5e94\u7528\u7a0b\u5e8f\uff08\u5305\u62ec\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff09\u7684\u6027\u80fd\u548c\u51c6\u786e\u6027\u4ea7\u751f\u6df1\u8fdc\u5f71\u54cd\u3002\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u5bf9\u4e8e\u83b7\u5f97\u53ef\u9760\u548c\u65e0\u504f\u7684\u7ed3\u679c\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<h2>\u4e0d\u5e73\u8861\u6570\u636e\u7684\u8d77\u6e90\u5386\u53f2\u53ca\u5176\u9996\u6b21\u63d0\u53ca<\/h2>\n<p>\u51e0\u5341\u5e74\u6765\uff0c\u4e0d\u5e73\u8861\u6570\u636e\u7684\u6982\u5ff5\u4e00\u76f4\u53d7\u5230\u5404\u4e2a\u79d1\u5b66\u9886\u57df\u7684\u5173\u6ce8\u3002\u7136\u800c\uff0c\u5b83\u6b63\u5f0f\u8fdb\u5165\u673a\u5668\u5b66\u4e60\u793e\u533a\u53ef\u4ee5\u8ffd\u6eaf\u5230 20 \u4e16\u7eaa 90 \u5e74\u4ee3\u3002\u8ba8\u8bba\u8fd9\u4e00\u95ee\u9898\u7684\u7814\u7a76\u8bba\u6587\u5f00\u59cb\u51fa\u73b0\uff0c\u5f3a\u8c03\u4e86\u5b83\u5bf9\u4f20\u7edf\u5b66\u4e60\u7b97\u6cd5\u63d0\u51fa\u7684\u6311\u6218\uff0c\u4ee5\u53ca\u9700\u8981\u4e13\u95e8\u7684\u6280\u672f\u6765\u6709\u6548\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002<\/p>\n<h2>\u5173\u4e8e\u4e0d\u5e73\u8861\u6570\u636e\u7684\u8be6\u7ec6\u4fe1\u606f\uff1a\u6269\u5c55\u4e3b\u9898<\/h2>\n<p>\u4e0d\u5e73\u8861\u6570\u636e\u51fa\u73b0\u5728\u8bb8\u591a\u73b0\u5b9e\u573a\u666f\u4e2d\uff0c\u4f8b\u5982\u533b\u7597\u8bca\u65ad\u3001\u6b3a\u8bc8\u68c0\u6d4b\u3001\u5f02\u5e38\u68c0\u6d4b\u548c\u7f55\u89c1\u4e8b\u4ef6\u9884\u6d4b\u3002\u5728\u8fd9\u4e9b\u60c5\u51b5\u4e0b\uff0c\u611f\u5174\u8da3\u7684\u4e8b\u4ef6\u4e0e\u975e\u4e8b\u4ef6\u5b9e\u4f8b\u76f8\u6bd4\u901a\u5e38\u5f88\u5c11\u89c1\uff0c\u4ece\u800c\u5bfc\u81f4\u7c7b\u522b\u5206\u5e03\u4e0d\u5e73\u8861\u3002<\/p>\n<p>\u4f20\u7edf\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u901a\u5e38\u5047\u8bbe\u6570\u636e\u96c6\u662f\u5e73\u8861\u7684\uff0c\u5bf9\u6240\u6709\u7c7b\u522b\u4e00\u89c6\u540c\u4ec1\u3002\u5f53\u5e94\u7528\u4e8e\u4e0d\u5e73\u8861\u6570\u636e\u65f6\uff0c\u8fd9\u4e9b\u7b97\u6cd5\u503e\u5411\u4e8e\u504f\u5411\u591a\u6570\u7c7b\u522b\uff0c\u5bfc\u81f4\u8bc6\u522b\u5c11\u6570\u7c7b\u522b\u5b9e\u4f8b\u7684\u6027\u80fd\u4e0d\u4f73\u3002\u8fd9\u79cd\u504f\u89c1\u80cc\u540e\u7684\u539f\u56e0\u662f\u5b66\u4e60\u8fc7\u7a0b\u7531\u6574\u4f53\u51c6\u786e\u5ea6\u9a71\u52a8\uff0c\u800c\u6574\u4f53\u51c6\u786e\u5ea6\u53d7\u5230\u8f83\u5927\u7c7b\u522b\u7684\u4e25\u91cd\u5f71\u54cd\u3002<\/p>\n<h2>\u4e0d\u5e73\u8861\u6570\u636e\u7684\u5185\u90e8\u7ed3\u6784\uff1a\u5176\u5de5\u4f5c\u539f\u7406<\/h2>\n<p>\u4e0d\u5e73\u8861\u6570\u636e\u53ef\u4ee5\u8868\u793a\u5982\u4e0b\uff1a<\/p>\n<pre><div class=\"bg-black rounded-md mb-4\"><div class=\"flex items-center relative text-gray-200 bg-gray-800 px-4 py-2 text-xs font-sans justify-between rounded-t-md\"><span>\u9c81\u963f<\/span><button class=\"flex ml-auto gap-2\"><svg stroke=\"currentColor\" fill=\"none\" stroke-width=\"2\" viewbox=\"0 0 24 24\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"h-4 w-4\" height=\"1em\" width=\"1em\" ><path d=\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\"><\/path><rect x=\"8\" y=\"2\" width=\"8\" height=\"4\" rx=\"1\" ry=\"1\"><\/rect><\/svg>\u590d\u5236\u4ee3\u7801<\/button><\/div><div class=\"p-4 overflow-y-auto\"><code class=\"!whitespace-pre hljs language-lua\" data-no-translation=\"\">|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n|       Class           |   Instances  |\n|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n|   Majority Class      |      N        |\n|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n|   Minority Class      |      M        |\n|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n<\/code><\/div><\/div><\/pre>\n<p>\u5176\u4e2dN\u8868\u793a\u591a\u6570\u7c7b\u7684\u5b9e\u4f8b\u6570\uff0cM\u8868\u793a\u5c11\u6570\u7c7b\u7684\u5b9e\u4f8b\u6570\u3002<\/p>\n<h2>\u4e0d\u5e73\u8861\u6570\u636e\u7684\u5173\u952e\u7279\u5f81\u5206\u6790<\/h2>\n<p>\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u4e0d\u5e73\u8861\u6570\u636e\uff0c\u5fc5\u987b\u5206\u6790\u4e00\u4e9b\u5173\u952e\u7279\u5f81\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u7c7b\u522b\u4e0d\u5e73\u8861\u7387<\/strong>\uff1a\u591a\u6570\u7c7b\u4e0e\u5c11\u6570\u7c7b\u7684\u5b9e\u4f8b\u6bd4\u4f8b\u3002\u53ef\u4ee5\u8868\u793a\u4e3aN\/M\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5c11\u6570\u7c7b\u7684\u7a00\u7f3a\u6027<\/strong>\uff1a\u5c11\u6570\u7c7b\u7684\u5b9e\u4f8b\u7684\u7edd\u5bf9\u6570\u91cf\u76f8\u5bf9\u4e8e\u6570\u636e\u96c6\u4e2d\u5b9e\u4f8b\u603b\u6570\u7684\u6bd4\u4f8b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u91cd\u53e0<\/strong>\uff1a\u5c11\u6570\u7c7b\u548c\u591a\u6570\u7c7b\u7684\u7279\u5f81\u5206\u5e03\u7684\u91cd\u53e0\u7a0b\u5ea6\u3002\u91cd\u53e0\u7a0b\u5ea6\u8d8a\u5927\uff0c\u5206\u7c7b\u96be\u5ea6\u8d8a\u5927\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6210\u672c\u654f\u611f\u5ea6<\/strong>\uff1a\u5c06\u4e0d\u540c\u7684\u9519\u8bef\u5206\u7c7b\u6210\u672c\u5206\u914d\u7ed9\u4e0d\u540c\u7684\u7c7b\u522b\u7684\u6982\u5ff5\uff0c\u7ed9\u4e88\u5c11\u6570\u7c7b\u522b\u66f4\u591a\u7684\u6743\u91cd\uff0c\u4ee5\u5b9e\u73b0\u5e73\u8861\u5206\u7c7b\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4e0d\u5e73\u8861\u6570\u636e\u7684\u7c7b\u578b<\/h2>\n<p>\u6839\u636e\u7c7b\u522b\u6570\u91cf\u548c\u7c7b\u522b\u4e0d\u5e73\u8861\u7a0b\u5ea6\uff0c\u4e0d\u5e73\u8861\u6570\u636e\u53ef\u5206\u4e3a\u4ee5\u4e0b\u4e0d\u540c\u7c7b\u578b\uff1a<\/p>\n<h3>\u6839\u636e\u73ed\u7ea7\u6570\u91cf\uff1a<\/h3>\n<ol>\n<li>\n<p><strong>\u4e8c\u8fdb\u5236\u4e0d\u5e73\u8861\u6570\u636e<\/strong>\uff1a\u4ec5\u5305\u542b\u4e24\u4e2a\u7c7b\u522b\u7684\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u4e00\u4e2a\u7c7b\u522b\u7684\u6570\u91cf\u660e\u663e\u591a\u4e8e\u53e6\u4e00\u4e2a\u7c7b\u522b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u591a\u7c7b\u4e0d\u5e73\u8861\u6570\u636e<\/strong>\uff1a\u5177\u6709\u591a\u4e2a\u7c7b\u522b\u7684\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u81f3\u5c11\u6709\u4e00\u4e2a\u7c7b\u522b\u4e0e\u5176\u4ed6\u7c7b\u522b\u76f8\u6bd4\u4ee3\u8868\u6027\u660e\u663e\u4e0d\u8db3\u3002<\/p>\n<\/li>\n<\/ol>\n<h3>\u6839\u636e\u7c7b\u522b\u4e0d\u5e73\u8861\u7a0b\u5ea6\uff1a<\/h3>\n<ol>\n<li>\n<p><strong>\u4e2d\u5ea6\u4e0d\u5e73\u8861<\/strong>\uff1a\u4e0d\u5e73\u8861\u7387\u76f8\u5bf9\u8f83\u4f4e\uff0c\u4e00\u822c\u57281\uff1a2\u81f31\uff1a5\u4e4b\u95f4\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4e25\u91cd\u5931\u8861<\/strong>\uff1a\u4e0d\u5e73\u8861\u7387\u5f88\u9ad8\uff0c\u5e38\u5e38\u8d85\u8fc71\uff1a10\u751a\u81f3\u66f4\u9ad8\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4e0d\u5e73\u8861\u6570\u636e\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848<\/h2>\n<h3>\u6570\u636e\u4e0d\u5e73\u8861\u7684\u95ee\u9898\uff1a<\/h3>\n<ol>\n<li>\n<p><strong>\u6709\u504f\u89c1\u7684\u5206\u7c7b<\/strong>\uff1a\u8be5\u6a21\u578b\u503e\u5411\u4e8e\u504f\u5411\u591a\u6570\u7c7b\u522b\uff0c\u5bfc\u81f4\u5c11\u6570\u7c7b\u522b\u7684\u8868\u73b0\u4e0d\u4f73\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5b66\u4e60\u56f0\u96be<\/strong>\uff1a\u7531\u4e8e\u7a00\u6709\u7c7b\u5b9e\u4f8b\u7684\u4ee3\u8868\u6027\u6709\u9650\uff0c\u4f20\u7edf\u7b97\u6cd5\u5f88\u96be\u4ece\u4e2d\u5b66\u4e60\u6a21\u5f0f\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8bef\u5bfc\u6027\u7684\u8bc4\u4f30\u6307\u6807<\/strong>\uff1a\u51c6\u786e\u5ea6\u53ef\u80fd\u662f\u4e00\u4e2a\u8bef\u5bfc\u6027\u7684\u6307\u6807\uff0c\u56e0\u4e3a\u6a21\u578b\u4ec5\u901a\u8fc7\u9884\u6d4b\u591a\u6570\u7c7b\u522b\u5c31\u80fd\u5b9e\u73b0\u9ad8\u7cbe\u5ea6\u3002<\/p>\n<\/li>\n<\/ol>\n<h3>\u89e3\u51b3\u65b9\u6848\uff1a<\/h3>\n<ol>\n<li>\n<p><strong>\u91cd\u91c7\u6837\u6280\u672f<\/strong>\uff1a\u5bf9\u591a\u6570\u7c7b\u8fdb\u884c\u6b20\u91c7\u6837\u6216\u5bf9\u5c11\u6570\u7c7b\u8fdb\u884c\u8fc7\u91c7\u6837\u53ef\u4ee5\u5e2e\u52a9\u5e73\u8861\u6570\u636e\u96c6\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7b97\u6cd5\u65b9\u6cd5<\/strong>\uff1a\u4e13\u4e3a\u5904\u7406\u4e0d\u5e73\u8861\u6570\u636e\u800c\u8bbe\u8ba1\u7684\u7279\u5b9a\u7b97\u6cd5\uff0c\u4f8b\u5982\u968f\u673a\u68ee\u6797\u3001SMOTE \u548c ADASYN\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6210\u672c\u654f\u611f\u5b66\u4e60<\/strong>\uff1a\u4fee\u6539\u5b66\u4e60\u8fc7\u7a0b\uff0c\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b\u5206\u914d\u4e0d\u540c\u7684\u9519\u8bef\u5206\u7c7b\u6210\u672c\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u96c6\u6210\u65b9\u6cd5<\/strong>\uff1a\u7ed3\u5408\u591a\u4e2a\u5206\u7c7b\u5668\u53ef\u4ee5\u63d0\u9ad8\u4e0d\u5e73\u8861\u6570\u636e\u7684\u6574\u4f53\u6027\u80fd\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u540c\u7c7b\u4ea7\u54c1\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u6570\u636e\u4e0d\u5e73\u8861<\/th>\n<th>\u5e73\u8861\u6570\u636e<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u7c7b\u522b\u5206\u5e03<\/td>\n<td>\u503e\u659c<\/td>\n<td>\u5236\u670d<\/td>\n<\/tr>\n<tr>\n<td>\u6311\u6218<\/td>\n<td>\u504f\u5411\u591a\u6570\u9636\u5c42<\/td>\n<td>\u5e73\u7b49\u5bf9\u5f85\u6240\u6709\u9636\u5c42<\/td>\n<\/tr>\n<tr>\n<td>\u5e38\u89c1\u89e3\u51b3\u65b9\u6848<\/td>\n<td>\u91cd\u65b0\u91c7\u6837\u3001\u7b97\u6cd5\u8c03\u6574<\/td>\n<td>\u6807\u51c6\u5b66\u4e60\u7b97\u6cd5<\/td>\n<\/tr>\n<tr>\n<td>\u6027\u80fd\u6307\u6807<\/td>\n<td>\u51c6\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1 \u5206\u6570<\/td>\n<td>\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u4e0d\u5e73\u8861\u6570\u636e\u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f<\/h2>\n<p>\u968f\u7740\u673a\u5668\u5b66\u4e60\u7814\u7a76\u7684\u8fdb\u5c55\uff0c\u53ef\u80fd\u4f1a\u51fa\u73b0\u66f4\u5148\u8fdb\u7684\u6280\u672f\u548c\u7b97\u6cd5\u6765\u89e3\u51b3\u4e0d\u5e73\u8861\u6570\u636e\u5e26\u6765\u7684\u6311\u6218\u3002\u7814\u7a76\u4eba\u5458\u6b63\u5728\u4e0d\u65ad\u63a2\u7d22\u65b0\u65b9\u6cd5\u6765\u63d0\u9ad8\u6a21\u578b\u5728\u4e0d\u5e73\u8861\u6570\u636e\u96c6\u4e0a\u7684\u6027\u80fd\uff0c\u4f7f\u5176\u66f4\u9002\u5e94\u73b0\u5b9e\u4e16\u754c\u7684\u573a\u666f\u3002<\/p>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u4e0e\u4e0d\u5e73\u8861\u6570\u636e\u5173\u8054<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u5728\u5404\u79cd\u6570\u636e\u5bc6\u96c6\u578b\u5e94\u7528\u4e2d\u8d77\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u7f51\u9875\u6293\u53d6\u548c\u533f\u540d\u5316\u3002\u867d\u7136\u4e0e\u4e0d\u5e73\u8861\u6570\u636e\u7684\u6982\u5ff5\u6ca1\u6709\u76f4\u63a5\u5173\u7cfb\uff0c\u4f46\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u7528\u4e8e\u5904\u7406\u53ef\u80fd\u6d89\u53ca\u4e0d\u5e73\u8861\u6570\u636e\u96c6\u7684\u5927\u89c4\u6a21\u6570\u636e\u6536\u96c6\u4efb\u52a1\u3002\u901a\u8fc7\u8f6e\u6362 IP \u5730\u5740\u548c\u7ba1\u7406\u6d41\u91cf\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u6709\u52a9\u4e8e\u9632\u6b62 IP \u7981\u4ee4\u5e76\u786e\u4fdd\u66f4\u987a\u7545\u5730\u4ece\u7f51\u7ad9\u6216 API \u4e2d\u63d0\u53d6\u6570\u636e\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173\u4e0d\u5e73\u8861\u6570\u636e\u53ca\u5176\u89e3\u51b3\u65b9\u6cd5\u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u63a2\u7d22\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ol>\n<li><a href=\"https:\/\/towardsdatascience.com\/dealing-with-imbalanced-data-in-machine-learning-7c4a692eda42\" target=\"_new\" rel=\"noopener nofollow\">\u8d70\u5411\u6570\u636e\u79d1\u5b66\u2014\u2014\u5904\u7406\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u4e0d\u5e73\u8861\u6570\u636e<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/auto_examples\/applications\/plot_tomography_reconstruction.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn \u6587\u6863 \u2013 \u5904\u7406\u4e0d\u5e73\u8861\u6570\u636e<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset\/\" target=\"_new\" rel=\"noopener nofollow\">\u673a\u5668\u5b66\u4e60\u7cbe\u901a\u2014\u2014\u5e94\u5bf9\u673a\u5668\u5b66\u4e60\u6570\u636e\u96c6\u4e2d\u4e0d\u5e73\u8861\u7c7b\u522b\u7684\u7b56\u7565<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/5128907\" target=\"_new\" rel=\"noopener nofollow\">IEEE \u77e5\u8bc6\u4e0e\u6570\u636e\u5de5\u7a0b\u5b66\u62a5 \u2013 \u4ece\u4e0d\u5e73\u8861\u6570\u636e\u4e2d\u5b66\u4e60<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468603,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477558","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Imbalanced Data: A Comprehensive Guide<\/mark>","faq_items":[{"question":"Question: What is imbalanced data?","answer":"<p>Answer: Imbalanced data refers to a situation where the distribution of classes within a dataset is highly skewed, with one class (the minority class) being significantly underrepresented compared to another (the majority class). This can pose challenges in various data-driven applications, including machine learning, leading to biased classification and lower performance on the minority class.<\/p>"},{"question":"Question: How did the issue of imbalanced data originate?","answer":"<p>Answer: The concept of imbalanced data has been recognized as a concern in various fields for years. However, its formal introduction into the machine learning community can be traced back to the 1990s when research papers began highlighting the challenges it posed to traditional learning algorithms.<\/p>"},{"question":"Question: What are the key features of imbalanced data?","answer":"<p>Answer: Key features of imbalanced data include the class imbalance ratio, the rareness of the minority class, the degree of data overlap between classes, and cost sensitivity. These features influence the learning process and the performance of machine learning models.<\/p>"},{"question":"Question: What are the types of imbalanced data?","answer":"<p>Answer: Imbalanced data can be categorized based on the number of classes and the degree of class imbalance. Based on the number of classes, it can be binary (two classes) or multiclass (multiple classes). Based on the degree of class imbalance, it can be moderate or severe.<\/p>"},{"question":"Question: What are the problems with imbalanced data, and how can they be solved?","answer":"<p>Answer: The problems with imbalanced data include biased classification, difficulty in learning patterns from rare classes, and misleading evaluation metrics. To address these issues, various solutions can be employed, such as resampling techniques, algorithmic approaches, and cost-sensitive learning.<\/p>"},{"question":"Question: How can proxy servers be associated with imbalanced data?","answer":"<p>Answer: While not directly related to imbalanced data, proxy servers play a crucial role in data-intensive applications, including data collection and web scraping. They can be used to handle large-scale data collection tasks, which may involve imbalanced datasets, by rotating IP addresses and managing traffic to prevent IP bans and ensure smoother data extraction.<\/p>"},{"question":"Question: What are the future perspectives and technologies related to imbalanced data?","answer":"<p>Answer: As machine learning research progresses, more advanced techniques and algorithms are likely to emerge to address the challenges of imbalanced data. Researchers are continuously exploring novel approaches to enhance model performance on imbalanced datasets and make them more adaptable to real-world scenarios.<\/p>"},{"question":"Question: Where can I find more information about imbalanced data?","answer":"<p>Answer: For more in-depth information and resources about imbalanced data and techniques to address it, you can explore the provided links in the article, which include helpful articles, documentation, and research papers.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477558","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\/477558\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468603"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=477558"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}