{"id":54627,"date":"2025-08-13T08:49:48","date_gmt":"2025-08-13T00:49:48","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/54627.html"},"modified":"2025-08-13T08:49:48","modified_gmt":"2025-08-13T00:49:48","slug":"sklearn-%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0-%e5%bc%82%e5%b8%b8%e5%80%bc%e6%a3%80%e6%b5%8b-%e5%b1%80%e9%83%a8%e5%bc%82%e5%b8%b8%e5%9b%a0%e5%ad%90%e7%ae%97%e6%b3%95lof","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/54627.html","title":{"rendered":"Sklearn \u673a\u5668\u5b66\u4e60 \u5f02\u5e38\u503c\u68c0\u6d4b \u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\u7b97\u6cd5LOF"},"content":{"rendered":"<p>\ud83d\udc96\u4eb2\u7231\u7684\u6280\u672f\u7231\u597d\u8005\u4eec\uff0c\u70ed\u70c8\u6b22\u8fce\u6765\u5230 Kant2048 \u7684\u535a\u5ba2\uff01\u6211\u662f Thomas Kant\uff0c\u5f88\u5f00\u5fc3\u80fd\u5728CSDN\u4e0a\u4e0e\u4f60\u4eec\u76f8\u9047\uff5e\ud83d\udc96<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250813004946-689be12a065ab.gif\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"><\/p>\n<p>\u672c\u535a\u5ba2\u7684\u7cbe\u534e\u4e13\u680f\uff1a \u3010\u81ea\u52a8\u5316\u6d4b\u8bd5\u3011 \u3010\u6d4b\u8bd5\u7ecf\u9a8c\u3011 \u3010\u4eba\u5de5\u667a\u80fd\u3011 \u3010Python\u3011<\/p>\n<hr>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/08\/20250813004946-689be12abd085.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"><\/p>\n<h2>Sklearn \u673a\u5668\u5b66\u4e60\u5f02\u5e38\u503c\u68c0\u6d4b\uff1a\u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\u7b97\u6cd5\uff08LOF\uff09<\/h2>\n<p>\u5728\u5b9e\u9645\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u4e2d\uff0c\u5f02\u5e38\u503c\u68c0\u6d4b\uff08Outlier Detection\uff09\u662f\u4e00\u4e2a\u4e0d\u53ef\u6216\u7f3a\u7684\u6b65\u9aa4\u3002\u65e0\u8bba\u662f\u7528\u4e8e\u4fe1\u7528\u5361\u6b3a\u8bc8\u8bc6\u522b\u3001\u7f51\u7edc\u5165\u4fb5\u68c0\u6d4b\uff0c\u8fd8\u662f\u5de5\u4e1a\u8bbe\u5907\u76d1\u63a7\uff0c\u53d1\u73b0\u201c\u4e0d\u5408\u7fa4\u201d\u7684\u6570\u636e\u70b9 \u90fd\u662f\u7406\u89e3\u7cfb\u7edf\u5f02\u5e38\u7684\u7b2c\u4e00\u6b65\u3002<\/p>\n<p>\u4eca\u5929\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u4ecb\u7ecd Sklearn \u4e2d\u7684 \u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\uff08Local Outlier Factor\uff0c\u7b80\u79f0 LOF\uff09 \u7b97\u6cd5\uff0c\u5e76\u901a\u8fc7\u4e30\u5bcc\u7684\u793a\u4f8b\u4e0e\u53ef\u89c6\u5316\uff0c\u6f14\u793a\u5b83\u5728\u4e8c\u7ef4\u7a7a\u95f4\u4e2d\u8bc6\u522b\u5c40\u90e8\u5f02\u5e38\u7684\u5f3a\u5927\u80fd\u529b\u3002<\/p>\n<hr>\n<h3>\ud83d\udccc \u4e00\u3001\u4ec0\u4e48\u662f\u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\uff08LOF\uff09\uff1f<\/h3>\n<h4>\u6838\u5fc3\u601d\u60f3\u89e3\u6790<\/h4>\n<p>\u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\uff08LOF\uff09\u662f\u4e00\u79cd \u57fa\u4e8e\u5bc6\u5ea6 \u7684\u5f02\u5e38\u503c\u68c0\u6d4b\u7b97\u6cd5\uff0c\u5b83\u4e0d\u5355\u7eaf\u4f9d\u8d56\u6837\u672c\u7684\u8ddd\u79bb\u6216\u5355\u7ef4\u5ea6\u7edf\u8ba1\uff0c\u800c\u662f\u901a\u8fc7\u5206\u6790\u67d0\u4e2a\u70b9\u4e0e\u5176\u90bb\u5c45\u4e4b\u95f4\u7684\u201c\u5c40\u90e8\u5bc6\u5ea6\u5dee\u5f02\u201d\u6765\u5224\u65ad\u662f\u5426\u5f02\u5e38\u3002<\/p>\n<p>\u901a\u4fd7\u6765\u8bb2\uff0cLOF \u8ba1\u7b97\u4e00\u4e2a\u70b9\u7684\u5bc6\u5ea6\u4e0e\u90bb\u5c45\u70b9\u5bc6\u5ea6\u7684\u6bd4\u503c\u3002\u5982\u679c\u8be5\u70b9\u5bc6\u5ea6\u663e\u8457\u4f4e\u4e8e\u90bb\u5c45\uff08\u6bd4\u503c\u8fdc\u5927\u4e8e 1\uff09\uff0c\u5219\u8be5\u70b9\u88ab\u89c6\u4e3a\u201c\u5c40\u90e8\u5f02\u5e38\u201d\u3002<\/p>\n<h4>\u4e0e\u4f20\u7edf\u65b9\u6cd5\u7684\u5bf9\u6bd4<\/h4>\n<table>\n<tr>\n   \u65b9\u6cd5<br \/>\n   \u662f\u5426\u57fa\u4e8e\u5bc6\u5ea6<br \/>\n   \u662f\u5426\u8003\u8651\u90bb\u57df\u7ed3\u6784<br \/>\n   \u662f\u5426\u652f\u6301\u591a\u7ef4\u6570\u636e<br \/>\n   \u9ad8\u7ef4\u8868\u73b0<br \/>\n  <\/tr>\n<tbody>\n<tr>\n<td>Z-score<\/td>\n<td>\u274c \u5426<\/td>\n<td>\u274c \u5426<\/td>\n<td>\u2705 \u652f\u6301<\/td>\n<td>\u274c \u6548\u679c\u5dee\uff08\u9010\u7ef4\u72ec\u7acb\u5206\u6790\uff09<\/td>\n<\/tr>\n<tr>\n<td>IQR<\/td>\n<td>\u274c \u5426<\/td>\n<td>\u274c \u5426<\/td>\n<td>\u2705 \u652f\u6301<\/td>\n<td>\u274c \u6548\u679c\u5dee\uff08\u4f9d\u8d56\u4e2d\u4f4d\u6570\uff09<\/td>\n<\/tr>\n<tr>\n<td>LOF<\/td>\n<td>\u2705 \u662f<\/td>\n<td>\u2705 \u662f<\/td>\n<td>\u2705 \u652f\u6301<\/td>\n<td>\u2705 \u7ed3\u5408\u964d\u7ef4\u66f4\u7a33\u5b9a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr>\n<h3>\ud83d\udd0d \u4e8c\u3001Sklearn \u4e2d\u7684 LOF \u5b9e\u73b0<\/h3>\n<p>Sklearn \u63d0\u4f9b\u4e86 sklearn.neighbors.LocalOutlierFactor \u7c7b\u6765\u5b9e\u73b0 LOF \u68c0\u6d4b\u3002\u652f\u6301\u65e0\u76d1\u7763\u5f02\u5e38\u8bc6\u522b\u3001\u4e5f\u652f\u6301\u201c\u534a\u76d1\u7763\u201d\u5f62\u5f0f\uff08\u4ec5\u4f7f\u7528\u6b63\u5e38\u6837\u672c\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\uff0c\u7528\u4e8e\u9884\u6d4b\u672a\u6765\u65b0\u6837\u672c\u662f\u5426\u5f02\u5e38\uff09\u3002<\/p>\n<h4>\u5bfc\u5165\u5fc5\u8981\u5e93<\/h4>\n<p><span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np<br \/>\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt<br \/>\n<span class=\"token keyword\">from<\/span> sklearn<span class=\"token punctuation\">.<\/span>neighbors <span class=\"token keyword\">import<\/span> LocalOutlierFactor<\/p>\n<hr>\n<h3>\ud83e\uddea \u4e09\u3001\u6784\u9020\u793a\u4f8b\u6570\u636e\u5e76\u53ef\u89c6\u5316<\/h3>\n<h4>\u6784\u9020\u4e8c\u7ef4\u6837\u672c<\/h4>\n<p><span class=\"token comment\"># \u6784\u9020\u6b63\u5e38\u6570\u636e\uff08\u805a\u96c6\u5728 2,2 \u9644\u8fd1\uff09<\/span><br \/>\nX_inliers <span class=\"token operator\">=<\/span> <span class=\"token number\">0.3<\/span> <span class=\"token operator\">*<\/span> np<span class=\"token punctuation\">.<\/span>random<span class=\"token punctuation\">.<\/span>randn<span class=\"token punctuation\">(<\/span><span class=\"token number\">100<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">+<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p><span class=\"token comment\"># \u6784\u9020\u79bb\u7fa4\u6570\u636e\uff08\u79bb\u7fa4\uff09<\/span><br \/>\nX_outliers <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>random<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb576\u6b21\uff0c\u70b9\u8d5e11\u6b21\uff0c\u6536\u85cf8\u6b21\u3002Sklearn \u4e2d\u7684\u5c40\u90e8\u5f02\u5e38\u56e0\u5b50 (LOF) \u7b97\u6cd5\u5728\u673a\u5668\u5b66\u4e60\u5f02\u5e38\u503c\u68c0\u6d4b\u4e2d\u7684\u5e94\u7528\u3002<\/p>\n","protected":false},"author":2,"featured_media":54625,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[5572,3676,207,427],"topic":[],"class_list":["post-54627","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-sklearn-","tag-sklearn","tag-207","tag-427"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Sklearn \u673a\u5668\u5b66\u4e60 \u5f02\u5e38\u503c\u68c0\u6d4b \u5c40\u90e8\u5f02\u5e38\u56e0\u5b50\u7b97\u6cd5LOF - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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