{"id":479722,"date":"2023-08-09T10:43:48","date_gmt":"2023-08-09T10:43:48","guid":{"rendered":""},"modified":"2023-09-05T11:19:26","modified_gmt":"2023-09-05T11:19:26","slug":"xgboost","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/xgboost\/","title":{"rendered":"XGBoost"},"content":{"rendered":"<p>XGBoost \u662f Extreme Gradient Boosting \u7684\u7f29\u5199\uff0c\u662f\u4e00\u79cd\u5c16\u7aef\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5f7b\u5e95\u6539\u53d8\u4e86\u9884\u6d4b\u5efa\u6a21\u548c\u6570\u636e\u5206\u6790\u9886\u57df\u3002\u5b83\u5c5e\u4e8e\u68af\u5ea6\u589e\u5f3a\u7b97\u6cd5\u7684\u8303\u7574\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u4e2a\u9886\u57df\u7684\u56de\u5f52\u3001\u5206\u7c7b\u548c\u6392\u540d\u7b49\u4efb\u52a1\u3002 XGBoost \u7684\u5f00\u53d1\u662f\u4e3a\u4e86\u514b\u670d\u4f20\u7edf boosting \u6280\u672f\u7684\u5c40\u9650\u6027\uff0c\u5b83\u7ed3\u5408\u4e86\u68af\u5ea6 boosting \u548c\u6b63\u5219\u5316\u6280\u672f\u7684\u4f18\u52bf\uff0c\u4ee5\u5b9e\u73b0\u5353\u8d8a\u7684\u9884\u6d4b\u51c6\u786e\u6027\u3002<\/p>\n<h2>XGBoost \u7684\u8d77\u6e90\u5386\u53f2<\/h2>\n<p>XGBoost \u7684\u65c5\u7a0b\u59cb\u4e8e 2014 \u5e74\uff0c\u5f53\u65f6\u534e\u76db\u987f\u5927\u5b66\u7814\u7a76\u5458 Tianqi Chen \u5c06\u8be5\u7b97\u6cd5\u5f00\u53d1\u4e3a\u5f00\u6e90\u9879\u76ee\u3002\u9996\u6b21\u63d0\u53ca XGBoost \u662f\u5728 2016 \u5e74 ACM SIGKDD \u4f1a\u8bae\u4e0a\u53d1\u8868\u7684\u9898\u4e3a\u201cXGBoost\uff1a\u53ef\u6269\u5c55\u7684\u6811\u63d0\u5347\u7cfb\u7edf\u201d\u7684\u7814\u7a76\u8bba\u6587\u3002\u8bba\u6587\u5c55\u793a\u4e86\u8be5\u7b97\u6cd5\u5728\u5404\u79cd\u673a\u5668\u5b66\u4e60\u7ade\u8d5b\u4e2d\u7684\u5353\u8d8a\u8868\u73b0\uff0c\u5e76\u5f3a\u8c03\u4e86\u5176\u9ad8\u6548\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u7684\u80fd\u529b\u3002<\/p>\n<h2>\u6709\u5173 XGBoost \u7684\u8be6\u7ec6\u4fe1\u606f<\/h2>\n<p>XGBoost \u7684\u6210\u529f\u53ef\u5f52\u56e0\u4e8e\u5176\u589e\u5f3a\u548c\u6b63\u5219\u5316\u6280\u672f\u7684\u72ec\u7279\u7ec4\u5408\u3002\u5b83\u91c7\u7528\u987a\u5e8f\u8bad\u7ec3\u8fc7\u7a0b\uff0c\u5176\u4e2d\u5f31\u5b66\u4e60\u5668\uff08\u901a\u5e38\u662f\u51b3\u7b56\u6811\uff09\u88ab\u987a\u5e8f\u8bad\u7ec3\uff0c\u6bcf\u4e2a\u65b0\u5b66\u4e60\u5668\u7684\u76ee\u6807\u662f\u7ea0\u6b63\u4ee5\u524d\u7684\u9519\u8bef\u3002\u6b64\u5916\uff0cXGBoost \u7ed3\u5408\u4e86\u6b63\u5219\u5316\u9879\u6765\u63a7\u5236\u6a21\u578b\u7684\u590d\u6742\u6027\u5e76\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\u3002\u8fd9\u79cd\u53cc\u91cd\u65b9\u6cd5\u4e0d\u4ec5\u63d0\u9ad8\u4e86\u9884\u6d4b\u51c6\u786e\u6027\uff0c\u8fd8\u6700\u5927\u9650\u5ea6\u5730\u964d\u4f4e\u4e86\u8fc7\u5ea6\u62df\u5408\u7684\u98ce\u9669\u3002<\/p>\n<h2>XGBoost\u7684\u5185\u90e8\u7ed3\u6784<\/h2>\n<p>XGBoost\u7684\u5185\u90e8\u7ed3\u6784\u53ef\u4ee5\u5206\u4e3a\u4ee5\u4e0b\u51e0\u4e2a\u5173\u952e\u7ec4\u4ef6\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u76ee\u6807\u51fd\u6570\uff1a<\/strong> XGBoost \u5b9a\u4e49\u4e86\u4e00\u4e2a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u9700\u8981\u4f18\u5316\u7684\u76ee\u6807\u51fd\u6570\u3002\u5e38\u89c1\u76ee\u6807\u5305\u62ec\u56de\u5f52\u4efb\u52a1\uff08\u4f8b\u5982\u5747\u65b9\u8bef\u5dee\uff09\u548c\u5206\u7c7b\u4efb\u52a1\uff08\u4f8b\u5982\u5bf9\u6570\u635f\u5931\uff09\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5f31\u5b66\u4e60\u8005\uff1a<\/strong> XGBoost \u4f7f\u7528\u51b3\u7b56\u6811\u4f5c\u4e3a\u5f31\u5b66\u4e60\u5668\u3002\u8fd9\u4e9b\u6811\u5f88\u6d45\uff0c\u6df1\u5ea6\u6709\u9650\uff0c\u4ece\u800c\u964d\u4f4e\u4e86\u8fc7\u5ea6\u62df\u5408\u7684\u98ce\u9669\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u68af\u5ea6\u63d0\u5347\uff1a<\/strong> XGBoost \u91c7\u7528\u68af\u5ea6\u63d0\u5347\uff0c\u5176\u4e2d\u6bcf\u68f5\u65b0\u6811\u7684\u6784\u9020\u90fd\u662f\u4e3a\u4e86\u6700\u5c0f\u5316\u635f\u5931\u51fd\u6570\u76f8\u5bf9\u4e8e\u5148\u524d\u6811\u7684\u9884\u6d4b\u7684\u68af\u5ea6\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6b63\u5219\u5316\uff1a<\/strong> \u5c06\u6b63\u5219\u5316\u9879\u6dfb\u52a0\u5230\u76ee\u6807\u51fd\u6570\u4e2d\u4ee5\u63a7\u5236\u6a21\u578b\u7684\u590d\u6742\u6027\u3002\u8fd9\u53ef\u4ee5\u9632\u6b62\u7b97\u6cd5\u5728\u6570\u636e\u4e2d\u62df\u5408\u566a\u58f0\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6811\u6728\u4fee\u526a\uff1a<\/strong> XGBoost \u5305\u542b\u4e00\u4e2a\u4fee\u526a\u6b65\u9aa4\uff0c\u53ef\u5728\u8bad\u7ec3\u671f\u95f4\u4ece\u6811\u4e0a\u79fb\u9664\u5206\u652f\uff0c\u8fdb\u4e00\u6b65\u589e\u5f3a\u6a21\u578b\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>XGBoost\u5173\u952e\u7279\u6027\u5206\u6790<\/h2>\n<p>XGBoost \u62e5\u6709\u51e0\u4e2a\u5173\u952e\u7279\u6027\uff0c\u8fd9\u4e9b\u7279\u6027\u4f7f\u5176\u5728\u9884\u6d4b\u5efa\u6a21\u65b9\u9762\u5177\u6709\u4f18\u8d8a\u6027\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u9ad8\u6027\u80fd\uff1a<\/strong> XGBoost \u4e13\u4e3a\u63d0\u9ad8\u6548\u7387\u548c\u53ef\u6269\u5c55\u6027\u800c\u8bbe\u8ba1\u3002\u5b83\u53ef\u4ee5\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u5e76\u6267\u884c\u5e76\u884c\u8ba1\u7b97\u4ee5\u52a0\u901f\u8bad\u7ec3\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7075\u6d3b\u6027\uff1a<\/strong> \u8be5\u7b97\u6cd5\u652f\u6301\u5404\u79cd\u76ee\u6807\u548c\u8bc4\u4f30\u6307\u6807\uff0c\u4f7f\u5176\u80fd\u591f\u9002\u5e94\u4e0d\u540c\u7684\u4efb\u52a1\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6b63\u5219\u5316\uff1a<\/strong> XGBoost \u7684\u6b63\u5219\u5316\u6280\u672f\u6709\u52a9\u4e8e\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\uff0c\u786e\u4fdd\u53ef\u9760\u7684\u6a21\u578b\u6cdb\u5316\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u529f\u80fd\u91cd\u8981\u6027\uff1a<\/strong> XGBoost \u63d0\u4f9b\u5bf9\u7279\u5f81\u91cd\u8981\u6027\u7684\u6d1e\u5bdf\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u4e86\u89e3\u9a71\u52a8\u9884\u6d4b\u7684\u53d8\u91cf\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5904\u7406\u7f3a\u5931\u6570\u636e\uff1a<\/strong> XGBoost \u53ef\u4ee5\u5728\u8bad\u7ec3\u548c\u9884\u6d4b\u671f\u95f4\u81ea\u52a8\u5904\u7406\u4e22\u5931\u7684\u6570\u636e\uff0c\u51cf\u5c11\u9884\u5904\u7406\u5de5\u4f5c\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>XGBoost \u7684\u7c7b\u578b<\/h2>\n<p>XGBoost \u6709\u9488\u5bf9\u7279\u5b9a\u4efb\u52a1\u5b9a\u5236\u7684\u4e0d\u540c\u53d8\u4f53\uff1a<\/p>\n<ul>\n<li><strong>XGBoost \u56de\u5f52\uff1a<\/strong> \u7528\u4e8e\u9884\u6d4b\u8fde\u7eed\u6570\u503c\u3002<\/li>\n<li><strong>XGBoost\u5206\u7c7b\uff1a<\/strong> \u7528\u4e8e\u4e8c\u5143\u548c\u591a\u7c7b\u5206\u7c7b\u4efb\u52a1\u3002<\/li>\n<li><strong>XGBoost\u6392\u540d\uff1a<\/strong> \u4e13\u4e3a\u6392\u540d\u4efb\u52a1\u800c\u8bbe\u8ba1\uff0c\u5176\u76ee\u6807\u662f\u6309\u91cd\u8981\u6027\u5bf9\u5b9e\u4f8b\u8fdb\u884c\u6392\u5e8f\u3002<\/li>\n<\/ul>\n<p>\u4ee5\u4e0b\u662f\u8868\u683c\u5f62\u5f0f\u7684\u6458\u8981\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>XGBoost \u56de\u5f52<\/td>\n<td>\u9884\u6d4b\u8fde\u7eed\u6570\u503c\u3002<\/td>\n<\/tr>\n<tr>\n<td>XGBoost\u5206\u7c7b<\/td>\n<td>\u5904\u7406\u4e8c\u5143\u548c\u591a\u7c7b\u5206\u7c7b\u3002<\/td>\n<\/tr>\n<tr>\n<td>XGBoost\u6392\u540d<\/td>\n<td>\u6309\u91cd\u8981\u6027\u5bf9\u5b9e\u4f8b\u8fdb\u884c\u6392\u540d\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>XGBoost \u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u548c\u89e3\u51b3\u65b9\u6848<\/h2>\n<p>XGBoost \u7684\u5e94\u7528\u8303\u56f4\u5e7f\u6cdb\uff0c\u5305\u62ec\u91d1\u878d\u3001\u533b\u7597\u4fdd\u5065\u3001\u8425\u9500\u7b49\u3002\u7136\u800c\uff0c\u7528\u6237\u53ef\u80fd\u4f1a\u9047\u5230\u53c2\u6570\u8c03\u6574\u548c\u6570\u636e\u4e0d\u5e73\u8861\u7b49\u6311\u6218\u3002\u91c7\u7528\u4ea4\u53c9\u9a8c\u8bc1\u548c\u4f18\u5316\u8d85\u53c2\u6570\u7b49\u6280\u672f\u53ef\u4ee5\u7f13\u89e3\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u6bd4\u8f83<\/h2>\n<p>\u4ee5\u4e0b\u662f XGBoost \u4e0e\u7c7b\u4f3c\u672f\u8bed\u7684\u5feb\u901f\u6bd4\u8f83\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>XGBoost<\/th>\n<th>\u968f\u673a\u68ee\u6797<\/th>\n<th>\u5149GBM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u5347\u538b\u6280\u672f<\/td>\n<td>\u68af\u5ea6\u63d0\u5347<\/td>\n<td>\u5957\u888b<\/td>\n<td>\u68af\u5ea6\u63d0\u5347<\/td>\n<\/tr>\n<tr>\n<td>\u6b63\u5219\u5316<\/td>\n<td>\u662f\uff08L1 \u548c L2\uff09<\/td>\n<td>\u4e0d<\/td>\n<td>\u662f\uff08\u57fa\u4e8e\u76f4\u65b9\u56fe\uff09<\/td>\n<\/tr>\n<tr>\n<td>\u7f3a\u5931\u6570\u636e\u5904\u7406<\/td>\n<td>\u662f\uff08\u81ea\u52a8\uff09<\/td>\n<td>\u5426\uff08\u9700\u8981\u9884\u5904\u7406\uff09<\/td>\n<td>\u662f\uff08\u81ea\u52a8\uff09<\/td>\n<\/tr>\n<tr>\n<td>\u8868\u73b0<\/td>\n<td>\u9ad8\u7684<\/td>\n<td>\u7f13\u548c<\/td>\n<td>\u9ad8\u7684<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u524d\u666f\u548c\u672a\u6765\u6280\u672f<\/h2>\n<p>XGBoost \u7684\u672a\u6765\u5145\u6ee1\u4ee4\u4eba\u5174\u594b\u7684\u53ef\u80fd\u6027\u3002\u7814\u7a76\u4eba\u5458\u548c\u5f00\u53d1\u4eba\u5458\u6b63\u5728\u4e0d\u65ad\u5b8c\u5584\u7b97\u6cd5\u5e76\u63a2\u7d22\u65b0\u6280\u672f\u4ee5\u63d0\u9ad8\u5176\u6027\u80fd\u3002\u6f5c\u5728\u7684\u5f00\u53d1\u9886\u57df\u5305\u62ec\u66f4\u9ad8\u6548\u7684\u5e76\u884c\u5316\u3001\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u96c6\u6210\u4ee5\u53ca\u6539\u8fdb\u5bf9\u5206\u7c7b\u7279\u5f81\u7684\u5904\u7406\u3002<\/p>\n<h2>XGBoost \u548c\u4ee3\u7406\u670d\u52a1\u5668<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u5728\u5404\u79cd\u5e94\u7528\u4e2d\u53d1\u6325\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u5305\u62ec\u7f51\u7edc\u6293\u53d6\u3001\u6570\u636e\u533f\u540d\u5316\u548c\u5728\u7ebf\u9690\u79c1\u3002 XGBoost \u53ef\u4ee5\u901a\u8fc7\u5b9e\u73b0\u9ad8\u6548\u7684\u6570\u636e\u6536\u96c6\u6765\u95f4\u63a5\u4ece\u4ee3\u7406\u670d\u52a1\u5668\u4e2d\u53d7\u76ca\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u6709\u901f\u7387\u9650\u5236\u7684 API \u65f6\u3002\u4ee3\u7406\u8f6e\u6362\u53ef\u4ee5\u5e2e\u52a9\u5747\u5300\u5206\u914d\u8bf7\u6c42\uff0c\u9632\u6b62 IP \u5c01\u7981\uff0c\u5e76\u786e\u4fdd\u7528\u4e8e\u8bad\u7ec3\u548c\u6d4b\u8bd5 XGBoost \u6a21\u578b\u7684\u7a33\u5b9a\u6570\u636e\u6d41\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173 XGBoost \u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u63a2\u7d22\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ul>\n<li><a href=\"https:\/\/xgboost.readthedocs.io\/en\/latest\/\" target=\"_new\" rel=\"noopener nofollow\">XGBoost \u6587\u6863<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/dmlc\/xgboost\" target=\"_new\" rel=\"noopener nofollow\">XGBoost GitHub \u5b58\u50a8\u5e93<\/a><\/li>\n<li><a href=\"https:\/\/homes.cs.washington.edu\/~tqchen\/pdf\/BoostedTree.pdf\" target=\"_new\" rel=\"noopener nofollow\">\u9648\u5929\u742a (Tianqi Chen) \u4ecb\u7ecd XGBoost<\/a><\/li>\n<\/ul>\n<p>XGBoost \u7ee7\u7eed\u6210\u4e3a\u673a\u5668\u5b66\u4e60\u4ece\u4e1a\u8005\u6b66\u5668\u5e93\u4e2d\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u63d0\u4f9b\u8de8\u4e0d\u540c\u9886\u57df\u7684\u51c6\u786e\u9884\u6d4b\u548c\u6709\u4ef7\u503c\u7684\u89c1\u89e3\u3002\u5176\u72ec\u7279\u7684\u589e\u5f3a\u548c\u6b63\u5219\u5316\u6280\u672f\u7ed3\u5408\u786e\u4fdd\u4e86\u7a33\u5065\u6027\u548c\u7cbe\u786e\u6027\uff0c\u4f7f\u5176\u6210\u4e3a\u73b0\u4ee3\u6570\u636e\u79d1\u5b66\u5de5\u4f5c\u6d41\u7a0b\u7684\u4e3b\u8981\u5185\u5bb9\u3002<\/p>","protected":false},"featured_media":0,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479722","wiki","type-wiki","status-publish","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>XGBoost: Enhancing Predictive Power with Extreme Gradient Boosting<\/mark>","faq_items":[{"question":"What is XGBoost and how does it work?","answer":"<p>XGBoost, or Extreme Gradient Boosting, is a state-of-the-art machine learning algorithm that combines gradient boosting and regularization techniques. It sequentially trains weak learners (often decision trees) to correct errors made by previous learners, enhancing predictive accuracy. Regularization is employed to prevent overfitting, resulting in robust and accurate models.<\/p>"},{"question":"How did XGBoost originate?","answer":"<p>XGBoost was developed by Tianqi Chen in 2014 and gained recognition through a research paper presented in 2016. This paper, titled \"XGBoost: A Scalable Tree Boosting System,\" highlighted the algorithm's exceptional performance in machine learning competitions and its ability to handle large datasets effectively.<\/p>"},{"question":"What are the key features of XGBoost?","answer":"<p>XGBoost boasts high performance, scalability, and flexibility. It utilizes shallow decision trees as weak learners and employs gradient boosting to optimize the objective function. Regularization techniques control model complexity, and the algorithm provides insights into feature importance. It can handle missing data and is applicable to various tasks like regression, classification, and ranking.<\/p>"},{"question":"How does XGBoost compare with other algorithms like Random Forests and LightGBM?","answer":"<p>In comparison with Random Forests and LightGBM, XGBoost uses gradient boosting, supports L1 and L2 regularization, and can handle missing data automatically. It generally exhibits higher performance and flexibility, making it a preferred choice in many scenarios.<\/p>"},{"question":"What types of XGBoost are available?","answer":"<p>XGBoost comes in three main types:<\/p><ul><li>XGBoost Regression: Predicts continuous numerical values.<\/li><li>XGBoost Classification: Handles binary and multiclass classification tasks.<\/li><li>XGBoost Ranking: Ranks instances by importance.<\/li><\/ul>"},{"question":"How can proxy servers be associated with XGBoost?","answer":"<p>Proxy servers can indirectly benefit XGBoost by enabling efficient data collection, particularly when dealing with APIs that have rate limits. Proxy rotation can help distribute requests evenly, preventing IP bans and ensuring a consistent stream of data for training and testing XGBoost models.<\/p>"},{"question":"What are the future prospects of XGBoost?","answer":"<p>The future of XGBoost holds promise in areas like improved parallelization, integration with deep learning frameworks, and enhanced handling of categorical features. Ongoing research and development are likely to lead to further advancements and applications.<\/p>"},{"question":"Where can I find more information about XGBoost?","answer":"<p>For more information about XGBoost, you can explore the following resources:<\/p><ul><li><a href=\"https:\/\/xgboost.readthedocs.io\/en\/latest\/\" target=\"_new\">XGBoost Documentation<\/a><\/li><li><a href=\"https:\/\/github.com\/dmlc\/xgboost\" target=\"_new\">XGBoost GitHub Repository<\/a><\/li><li><a href=\"https:\/\/homes.cs.washington.edu\/~tqchen\/pdf\/BoostedTree.pdf\" target=\"_new\">Introduction to XGBoost by Tianqi Chen<\/a><\/li><\/ul>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/479722","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\/479722\/revisions"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=479722"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}