{"id":4065,"date":"2025-10-18T11:26:20","date_gmt":"2025-10-18T03:26:20","guid":{"rendered":"http:\/\/viplao.com\/?p=4065"},"modified":"2025-10-18T11:26:23","modified_gmt":"2025-10-18T03:26:23","slug":"%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3","status":"publish","type":"post","link":"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/","title":{"rendered":"\u3010PYTHON\u5b9e\u8df5\u6848\u4f8b\u3011\u7535\u5546\u7528\u6237\u6d41\u5931\u9884\u6d4b\u6a21\u578b\u4ee3\u7801\u6df1\u5ea6\u89e3\u6790"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_71 counter-hierarchy ez-toc-counter ez-toc-grey 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href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%94%B9_%E6%A8%A1%E5%9D%974%EF%BC%9Aanalyze_feature_importance_%E2%80%94%E2%80%94_%E4%BB%8E%E6%A8%A1%E5%9E%8B%E5%88%B0%E4%B8%9A%E5%8A%A1%E6%B4%9E%E5%AF%9F%E7%9A%84%E6%A1%A5%E6%A2%81\" title=\"\ud83d\udd39 \u6a21\u57574\uff1aanalyze_feature_importance()&nbsp;\u2014\u2014&nbsp;\u4ece\u6a21\u578b\u5230\u4e1a\u52a1\u6d1e\u5bdf\u7684\u6865\u6881\">\ud83d\udd39 \u6a21\u57574\uff1aanalyze_feature_importance()&nbsp;\u2014\u2014&nbsp;\u4ece\u6a21\u578b\u5230\u4e1a\u52a1\u6d1e\u5bdf\u7684\u6865\u6881<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%E2%9C%85_%E5%AE%9E%E7%8E%B0%E6%96%B9%E5%BC%8F%EF%BC%9A\" title=\"\u2705 \u5b9e\u73b0\u65b9\u5f0f\uff1a\">\u2705 \u5b9e\u73b0\u65b9\u5f0f\uff1a<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%92%A1_%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%80%E6%AD%A5%E6%9E%81%E5%85%B6%E9%87%8D%E8%A6%81%EF%BC%9F\" title=\"\ud83d\udca1 \u4e3a\u4ec0\u4e48\u8fd9\u4e00\u6b65\u6781\u5176\u91cd\u8981\uff1f\">\ud83d\udca1 \u4e3a\u4ec0\u4e48\u8fd9\u4e00\u6b65\u6781\u5176\u91cd\u8981\uff1f<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%94%B9_%E6%A8%A1%E5%9D%975%EF%BC%9Agenerate_churn_prediction_report_%E2%80%94%E2%80%94_%E8%AE%A9%E6%95%B0%E6%8D%AE%E8%AF%B4%E8%AF%9D%EF%BC%8C%E6%8E%A8%E5%8A%A8%E5%86%B3%E7%AD%96%E8%90%BD%E5%9C%B0\" title=\"\ud83d\udd39 \u6a21\u57575\uff1agenerate_churn_prediction_report()&nbsp;\u2014\u2014&nbsp;\u8ba9\u6570\u636e\u8bf4\u8bdd\uff0c\u63a8\u52a8\u51b3\u7b56\u843d\u5730\">\ud83d\udd39 \u6a21\u57575\uff1agenerate_churn_prediction_report()&nbsp;\u2014\u2014&nbsp;\u8ba9\u6570\u636e\u8bf4\u8bdd\uff0c\u63a8\u52a8\u51b3\u7b56\u843d\u5730<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%E2%9C%85_%E6%8A%A5%E5%91%8A%E7%BB%93%E6%9E%84%E4%B8%93%E4%B8%9A%E4%B8%94%E5%AE%9E%E7%94%A8%EF%BC%9A\" title=\"\u2705 \u62a5\u544a\u7ed3\u6784\u4e13\u4e1a\u4e14\u5b9e\u7528\uff1a\">\u2705 \u62a5\u544a\u7ed3\u6784\u4e13\u4e1a\u4e14\u5b9e\u7528\uff1a<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%92%AC_%E5%85%B8%E5%9E%8B%E4%B8%9A%E5%8A%A1%E5%BB%BA%E8%AE%AE%E7%A4%BA%E4%BE%8B%EF%BC%9A\" title=\"\ud83d\udcac \u5178\u578b\u4e1a\u52a1\u5efa\u8bae\u793a\u4f8b\uff1a\">\ud83d\udcac \u5178\u578b\u4e1a\u52a1\u5efa\u8bae\u793a\u4f8b\uff1a<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%8E%AF_%E4%B8%89%E3%80%81%E4%BB%8E%E4%B8%9A%E5%8A%A1%E8%A7%86%E8%A7%92%E7%9C%8B%E8%BF%99%E4%B8%AA%E9%A1%B9%E7%9B%AE%E7%9A%84%E5%AE%9E%E6%88%98%E4%BB%B7%E5%80%BC\" title=\"\ud83c\udfaf \u4e09\u3001\u4ece\u4e1a\u52a1\u89c6\u89d2\u770b\u8fd9\u4e2a\u9879\u76ee\u7684\u5b9e\u6218\u4ef7\u503c\">\ud83c\udfaf \u4e09\u3001\u4ece\u4e1a\u52a1\u89c6\u89d2\u770b\u8fd9\u4e2a\u9879\u76ee\u7684\u5b9e\u6218\u4ef7\u503c<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%9B%A0%EF%B8%8F_%E5%9B%9B%E3%80%81%E5%8F%AF%E6%89%A9%E5%B1%95%E6%80%A7%E4%B8%8E%E7%94%9F%E4%BA%A7%E5%8C%96%E5%BB%BA%E8%AE%AE\" title=\"\ud83d\udee0\ufe0f \u56db\u3001\u53ef\u6269\u5c55\u6027\u4e0e\u751f\u4ea7\u5316\u5efa\u8bae\">\ud83d\udee0\ufe0f \u56db\u3001\u53ef\u6269\u5c55\u6027\u4e0e\u751f\u4ea7\u5316\u5efa\u8bae<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%E2%9C%85_%E5%BD%93%E5%89%8D%E4%BC%98%E7%82%B9%EF%BC%9A\" title=\"\u2705 \u5f53\u524d\u4f18\u70b9\uff1a\">\u2705 \u5f53\u524d\u4f18\u70b9\uff1a<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%94%A7_%E7%94%9F%E4%BA%A7%E7%8E%AF%E5%A2%83%E6%94%B9%E8%BF%9B%E5%BB%BA%E8%AE%AE%EF%BC%9A\" title=\"\ud83d\udd27 \u751f\u4ea7\u73af\u5883\u6539\u8fdb\u5efa\u8bae\uff1a\">\ud83d\udd27 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href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%F0%9F%8E%81_%E7%BB%93%E8%AF%AD\" title=\"\ud83c\udf81 \u7ed3\u8bed\">\ud83c\udf81 \u7ed3\u8bed<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/#%E7%94%9F%E6%88%90%E6%97%B6%E9%97%B4_2025-10-12_22_49_22\" title=\"\u751f\u6210\u65f6\u95f4 2025-10-12 224922\">\u751f\u6210\u65f6\u95f4 2025-10-12 224922<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%A7%A9_%E4%B8%80%E3%80%81%E6%95%B4%E4%BD%93%E6%9E%B6%E6%9E%84%E6%A6%82%E8%A7%88\"><\/span>\ud83e\udde9 \u4e00\u3001\u6574\u4f53\u67b6\u6784\u6982\u89c8<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>\u8be5\u811a\u672c\u662f\u4e00\u4e2a\u5b8c\u6574\u7684 <strong>\u7aef\u5230\u7aef\uff08End-to-End\uff09\u7528\u6237\u6d41\u5931\u9884\u6d4b\u9879\u76ee\u539f\u578b<\/strong>\uff0c\u6db5\u76d6\u4e86\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>\u9636\u6bb5<\/th><th>\u529f\u80fd<\/th><\/tr><\/thead><tbody><tr><td>\u2705 \u6570\u636e\u751f\u6210<\/td><td>\u6a21\u62df\u771f\u5b9e\u7535\u5546\u5e73\u53f0\u7528\u6237\u884c\u4e3a\u6570\u636e<\/td><\/tr><tr><td>\u2705 \u6570\u636e\u9884\u5904\u7406<\/td><td>\u7f16\u7801\u3001\u7279\u5f81\u5de5\u7a0b\u3001\u8bad\u7ec3\u96c6\u5212\u5206<\/td><\/tr><tr><td>\u2705 \u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30<\/td><td>\u5bf9\u6bd4\u903b\u8f91\u56de\u5f52 vs. \u968f\u673a\u68ee\u6797<\/td><\/tr><tr><td>\u2705 \u7279\u5f81\u91cd\u8981\u6027\u5206\u6790<\/td><td>\u627e\u51fa\u9a71\u52a8\u6d41\u5931\u7684\u6838\u5fc3\u56e0\u7d20<\/td><\/tr><tr><td>\u2705 \u81ea\u52a8\u5316\u62a5\u544a\u751f\u6210<\/td><td>\u8f93\u51fa\u6587\u672c\u62a5\u544a + \u56fe\u8868\uff0c\u4fbf\u4e8e\u51b3\u7b56<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\ud83c\udfaf <strong>\u6838\u5fc3\u76ee\u6807<\/strong>\uff1a\u8bc6\u522b\u9ad8\u98ce\u9669\u6d41\u5931\u7528\u6237\uff0c\u5e76\u901a\u8fc7\u5173\u952e\u7279\u5f81\u6d1e\u5bdf\u5236\u5b9a\u7cbe\u51c6\u5e72\u9884\u7b56\u7565\u3002<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%93%A6_%E4%BA%8C%E3%80%81%E6%A8%A1%E5%9D%97%E8%AF%A6%E8%A7%A3%E4%B8%8E%E4%B8%93%E4%B8%9A%E7%82%B9%E8%AF%84\"><\/span>\ud83d\udce6 \u4e8c\u3001\u6a21\u5757\u8be6\u89e3\u4e0e\u4e13\u4e1a\u70b9\u8bc4<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%94%B9_%E6%A8%A1%E5%9D%971%EF%BC%9Agenerate_sample_churn_datan_users_%E2%80%94%E2%80%94_%E9%AB%98%E8%B4%A8%E9%87%8F%E6%A8%A1%E6%8B%9F%E6%95%B0%E6%8D%AE%E7%9A%84%E8%AE%BE%E8%AE%A1%E5%93%B2%E5%AD%A6\"><\/span>\ud83d\udd39 \u6a21\u57571\uff1a<code>generate_sample_churn_data(n_users)<\/code>&nbsp;\u2014\u2014&nbsp;<strong>\u9ad8\u8d28\u91cf\u6a21\u62df\u6570\u636e\u7684\u8bbe\u8ba1\u54f2\u5b66<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9C%85_%E5%81%9A%E5%BE%97%E5%A5%BD%E7%9A%84%E5%9C%B0%E6%96%B9%EF%BC%9A\"><\/span>\u2705 \u505a\u5f97\u597d\u7684\u5730\u65b9\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol>\n<li><strong>\u57fa\u4e8e\u4e1a\u52a1\u903b\u8f91\u6784\u9020\u6570\u636e<\/strong>\n<ul>\n<li>\u4e0d\u662f\u968f\u673a\u4e71\u9020\uff0c\u800c\u662f\u6839\u636e\u201c\u4f1a\u5458\u7b49\u7ea7\u8d8a\u9ad8 \u2192 \u6d88\u8d39\u8d8a\u591a\u3001\u6d3b\u8dc3\u5ea6\u8d8a\u9ad8\u3001\u6d41\u5931\u6982\u7387\u8d8a\u4f4e\u201d\u7684\u7535\u5546\u5e38\u8bc6\u8bbe\u8ba1\u3002<\/li>\n\n\n\n<li>\u4f7f\u7528&nbsp;<code>Poisson<\/code>&nbsp;\u5206\u5e03\u6a21\u62df\u8ba2\u5355\u6570\uff08\u79bb\u6563\u8ba1\u6570\uff09\u3001<code>LogNormal<\/code>&nbsp;\u6a21\u62df\u6d88\u8d39\u91d1\u989d\uff08\u53f3\u504f\u5206\u5e03\uff09\uff0c\u7b26\u5408\u73b0\u5b9e\u89c4\u5f8b\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5f15\u5165\u591a\u7ef4\u7528\u6237\u753b\u50cf<\/strong>\n<ul>\n<li>\u201c\u4eba\u201d\uff1a\u5e74\u9f84\u3001\u6027\u522b<\/li>\n\n\n\n<li>\u201c\u8d27\u201d\uff1a\u603b\u6d88\u8d39\u3001AOV\uff08\u5e73\u5747\u8ba2\u5355\u4ef7\u503c\uff09<\/li>\n\n\n\n<li>\u201c\u573a\u201d\uff1a\u57ce\u5e02\u7b49\u7ea7\u3001\u8bbf\u95ee\u9891\u6b21\u3001\u5ba2\u670d\u4e92\u52a8<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u6784\u5efa\u5408\u7406\u7684\u6d41\u5931\u6807\u7b7e\u673a\u5236<\/strong>\n<ul>\n<li>\u6d41\u5931\u4e0d\u662f\u7b80\u5355\u968f\u673a\uff0c\u800c\u662f\u57fa\u4e8e\u591a\u4e2a\u98ce\u9669\u4fe1\u53f7\u53e0\u52a0\uff1aPython\u7f16\u8f91<code>if days_since_last_purchase &gt; 90: churn_prob += 0.4 # \u5f3a\u4fe1\u53f7 if cart_abandonment_rate &gt; 0.7: churn_prob += 0.15 # \u884c\u4e3a\u72b9\u8c6b<\/code><\/li>\n\n\n\n<li>\u52a0\u5165\u566a\u58f0\u9879&nbsp;<code>np.random.normal(0, 0.1)<\/code>&nbsp;\u63d0\u5347\u6cdb\u5316\u80fd\u529b\uff0c\u907f\u514d\u5b8c\u7f8e\u53ef\u5206\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u884d\u751f\u5b57\u6bb5\u8ba1\u7b97\u5408\u7406<\/strong>\n<ul>\n<li><code>tenure_days<\/code>\uff08\u6ce8\u518c\u65f6\u957f\uff09\u662f\u91cd\u8981\u7684\u751f\u547d\u5468\u671f\u6307\u6807\uff0c\u5728\u7559\u5b58\u5206\u6790\u4e2d\u975e\u5e38\u5173\u952e\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%92%A1_%E4%B8%93%E4%B8%9A%E5%BB%BA%E8%AE%AE%EF%BC%88%E8%BF%9B%E9%98%B6%E4%BC%98%E5%8C%96%E6%96%B9%E5%90%91%EF%BC%89%EF%BC%9A\"><\/span>\ud83d\udca1 \u4e13\u4e1a\u5efa\u8bae\uff08\u8fdb\u9636\u4f18\u5316\u65b9\u5411\uff09\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul>\n<li>\u771f\u5b9e\u573a\u666f\u4e2d\u5e94\u52a0\u5165&nbsp;<strong>\u65f6\u95f4\u7a97\u53e3\u5207\u7247<\/strong>\uff08\u5982\u7528\u8fc7\u53bb6\u4e2a\u6708\u6570\u636e\u9884\u6d4b\u672a\u67653\u4e2a\u6708\u662f\u5426\u6d41\u5931\uff09<\/li>\n\n\n\n<li>\u53ef\u589e\u52a0\u66f4\u591a\u884c\u4e3a\u7279\u5f81\uff1a\u52a0\u8d2d\u5546\u54c1\u7c7b\u76ee\u504f\u597d\u3001\u9875\u9762\u505c\u7559\u65f6\u957f\u3001\u4f18\u60e0\u5238\u4f7f\u7528\u8def\u5f84\u7b49<\/li>\n\n\n\n<li>\u6807\u7b7e\u5b9a\u4e49\u66f4\u7cbe\u7ec6\uff1a\u533a\u5206\u201c\u6682\u65f6\u4f11\u7720\u201d\u548c\u201c\u6c38\u4e45\u6d41\u5931\u201d<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%94%B9_%E6%A8%A1%E5%9D%972%EF%BC%9Apreprocess_datadf_%E2%80%94%E2%80%94_%E7%BB%93%E6%9E%84%E5%8C%96%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B%E5%AE%9E%E8%B7%B5\"><\/span>\ud83d\udd39 \u6a21\u57572\uff1a<code>preprocess_data(df)<\/code>&nbsp;\u2014\u2014&nbsp;<strong>\u7ed3\u6784\u5316\u7279\u5f81\u5de5\u7a0b\u5b9e\u8df5<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9C%85_%E5%85%B3%E9%94%AE%E6%93%8D%E4%BD%9C%EF%BC%9A\"><\/span>\u2705 \u5173\u952e\u64cd\u4f5c\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<pre class=\"wp-block-preformatted\">Python\u7f16\u8f91<code>le_gender = LabelEncoder()\ndf_processed['gender_encoded'] = le_gender.fit_transform(df_processed['gender'])<\/code><\/pre>\n\n\n\n<ul>\n<li>\u5c06\u5206\u7c7b\u53d8\u91cf\u7f16\u7801\u4e3a\u6570\u503c\u578b\uff0c\u6ee1\u8db3\u673a\u5668\u5b66\u4e60\u8f93\u5165\u8981\u6c42\u3002<\/li>\n\n\n\n<li>\u9009\u62e9&nbsp;<code>LabelEncoder<\/code>&nbsp;\u662f\u5408\u9002\u7684\uff0c\u56e0\u4e3a\u6027\u522b\/\u57ce\u5e02\u7b49\u7ea7\u867d\u4e3a\u7c7b\u522b\uff0c\u4f46\u65e0\u663e\u8457\u987a\u5e8f\u542b\u4e49\uff08\u82e5\u6709\u5e8f\u53ef\u7528&nbsp;<code>OrdinalEncoder<\/code>\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9A%A0%EF%B8%8F_%E6%B3%A8%E6%84%8F%E4%BA%8B%E9%A1%B9%EF%BC%9A\"><\/span>\u26a0\ufe0f \u6ce8\u610f\u4e8b\u9879\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul>\n<li><code>membership_tier<\/code>&nbsp;\u662f\u6709\u5e8f\u7c7b\u522b\uff08Bronze &lt; Silver &lt; Gold\uff09\uff0c\u7406\u60f3\u505a\u6cd5\u662f\u624b\u52a8\u6620\u5c04\u4e3a&nbsp;<code>[0,1,2]<\/code>&nbsp;\u6216\u4f7f\u7528&nbsp;<code>OrdinalEncoder<\/code>\uff0c\u800c\u975e\u4f9d\u8d56&nbsp;<code>LabelEncoder<\/code>&nbsp;\u7684\u5b57\u6bcd\u6392\u5e8f\uff08&#8217;B&#8217;,&#8217;G&#8217;,&#8217;S&#8217; \u2192 0,1,2 \u6b63\u597d\u5bf9\u4e0a\u7eaf\u5c5e\u5de7\u5408\uff09\u3002<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9C%85_%E7%89%B9%E5%BE%81%E9%80%89%E6%8B%A9%E5%90%88%E7%90%86%EF%BC%9A\"><\/span>\u2705 \u7279\u5f81\u9009\u62e9\u5408\u7406\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>\u4fdd\u7559\u4e86\u4ee5\u4e0b\u51e0\u7c7b\u5178\u578b\u7279\u5f81\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>\u7c7b\u578b<\/th><th>\u793a\u4f8b<\/th><\/tr><\/thead><tbody><tr><td>\u4eba\u53e3\u7edf\u8ba1<\/td><td>age, gender<\/td><\/tr><tr><td>\u7528\u6237\u8eab\u4efd<\/td><td>membership_tier, location_city_tier<\/td><\/tr><tr><td>\u6d88\u8d39\u884c\u4e3a<\/td><td>total_orders, total_spent, avg_order_value<\/td><\/tr><tr><td>\u6d3b\u8dc3\u5ea6<\/td><td>monthly_visits, days_since_last_purchase<\/td><\/tr><tr><td>\u98ce\u9669\u4fe1\u53f7<\/td><td>cart_abandonment_rate, customer_service_contacts<\/td><\/tr><tr><td>\u5fe0\u8bda\u5ea6<\/td><td>tenure_days, discount_usage_freq<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\ud83d\udc49 \u8fd9\u6b63\u662f\u5178\u578b\u7684 <strong>RFM\u6269\u5c55\u6a21\u578b\uff08Recency-Frequency-Monetary + Contextual Features\uff09<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%94%B9_%E6%A8%A1%E5%9D%973%EF%BC%9Atrain_and_evaluate_modelsX_y_%E2%80%94%E2%80%94_%E5%8F%8C%E6%A8%A1%E5%9E%8B%E5%AF%B9%E6%AF%94%E7%AD%96%E7%95%A5%E7%A7%91%E5%AD%A6\"><\/span>\ud83d\udd39 \u6a21\u57573\uff1a<code>train_and_evaluate_models(X, y)<\/code>&nbsp;\u2014\u2014&nbsp;<strong>\u53cc\u6a21\u578b\u5bf9\u6bd4\u7b56\u7565\u79d1\u5b66<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9C%85_%E8%AE%BE%E8%AE%A1%E4%BA%AE%E7%82%B9%EF%BC%9A\"><\/span>\u2705 \u8bbe\u8ba1\u4eae\u70b9\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol>\n<li><strong>\u53cc\u6a21\u578b\u5bf9\u6bd4\uff1aLR vs RF<\/strong>\n<ul>\n<li><strong>\u903b\u8f91\u56de\u5f52\uff08LR\uff09<\/strong>\uff1a\u89e3\u91ca\u6027\u5f3a\uff0c\u9002\u5408\u521d\u671f\u5f52\u56e0\u5206\u6790<\/li>\n\n\n\n<li><strong>\u968f\u673a\u68ee\u6797\uff08RF\uff09<\/strong>\uff1a\u975e\u7ebf\u6027\u80fd\u529b\u5f3a\uff0c\u6355\u6349\u4ea4\u4e92\u6548\u5e94\uff0c\u901a\u5e38\u6027\u80fd\u66f4\u597d<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u6b63\u786e\u7684\u6570\u636e\u5904\u7406\u65b9\u5f0f<\/strong>\n<ul>\n<li>LR \u4f7f\u7528&nbsp;<code>StandardScaler<\/code>&nbsp;\u5f52\u4e00\u5316\uff08\u5fc5\u8981\uff09<\/li>\n\n\n\n<li>RF \u76f4\u63a5\u4f7f\u7528\u539f\u59cb\u6570\u636e\uff08\u6811\u6a21\u578b\u4e0d\u53d7\u91cf\u7eb2\u5f71\u54cd\uff09<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5206\u5c42\u62bd\u6837\uff08stratify=y\uff09<\/strong>Python\u7f16\u8f91<code>train_test_split(..., stratify=y)<\/code>\u786e\u4fdd\u8bad\u7ec3\/\u6d4b\u8bd5\u96c6\u4e2d\u6b63\u8d1f\u6837\u672c\u6bd4\u4f8b\u4e00\u81f4\uff0c\u5c24\u5176\u5728\u4e0d\u5e73\u8861\u6570\u636e\u4e0b\u81f3\u5173\u91cd\u8981\u3002<\/li>\n\n\n\n<li><strong>\u5168\u9762\u8bc4\u4f30\u6307\u6807\u4f53\u7cfb<\/strong>Python\u7f16\u8f91<code>accuracy, precision, recall, f1, auc, confusion_matrix, classification_report<\/code>\ud83d\udc49 \u5b8c\u5168\u8986\u76d6\u5206\u7c7b\u4efb\u52a1\u8bc4\u4ef7\u7ef4\u5ea6\u3002<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%93%8A_%E6%8C%87%E6%A0%87%E8%A7%A3%E8%AF%BB%EF%BC%88%E9%9D%A2%E5%90%91%E4%B8%9A%E5%8A%A1%EF%BC%89%EF%BC%9A\"><\/span>\ud83d\udcca \u6307\u6807\u89e3\u8bfb\uff08\u9762\u5411\u4e1a\u52a1\uff09\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>\u6307\u6807<\/th><th>\u6570\u5b66\u610f\u4e49<\/th><th>\u4e1a\u52a1\u542b\u4e49<\/th><\/tr><\/thead><tbody><tr><td>Accuracy<\/td><td>(TP+TN)\/Total<\/td><td>\u6574\u4f53\u9884\u6d4b\u51c6\u786e\u7387<\/td><\/tr><tr><td>Precision<\/td><td>TP\/(TP+FP)<\/td><td>\u201c\u6211\u4eec\u8ba4\u4e3a\u8981\u6d41\u5931\u7684\u4eba\u91cc\uff0c\u771f\u6d41\u5931\u7684\u6bd4\u4f8b\u201d \u2014\u2014 \u5e72\u9884\u6210\u672c\u6548\u7387<\/td><\/tr><tr><td>Recall<\/td><td>TP\/(TP+FN)<\/td><td>\u201c\u6240\u6709\u5b9e\u9645\u6d41\u5931\u7684\u4eba\u4e2d\uff0c\u6211\u4eec\u6293\u5230\u4e86\u591a\u5c11\u201d \u2014\u2014 \u98ce\u63a7\u8986\u76d6\u7387<\/td><\/tr><tr><td>F1-Score<\/td><td>2\u00d7P\u00d7R\/(P+R)<\/td><td>P \u548c R \u7684\u8c03\u548c\u5e73\u5747\uff0c\u7efc\u5408\u6307\u6807<\/td><\/tr><tr><td>AUC-ROC<\/td><td>\u66f2\u7ebf\u4e0b\u9762\u79ef<\/td><td>\u6a21\u578b\u533a\u5206\u80fd\u529b\uff0c\u4e0d\u4f9d\u8d56\u9608\u503c<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>\ud83d\udca1 <strong>\u4e1a\u52a1\u6743\u8861\u5efa\u8bae<\/strong>\uff1a<\/p>\n\n\n\n<ul>\n<li>\u82e5\u8fd0\u8425\u8d44\u6e90\u6709\u9650 \u2192 \u4f18\u5148\u63d0\u5347&nbsp;<strong>Precision<\/strong>\uff08\u51cf\u5c11\u8bef\u6740\uff09<\/li>\n\n\n\n<li>\u82e5\u5ba2\u6237\u4ef7\u503c\u6781\u9ad8 \u2192 \u4f18\u5148\u63d0\u5347&nbsp;<strong>Recall<\/strong>\uff08\u5b81\u53ef\u9519\u6740\uff0c\u4e0d\u53ef\u653e\u8fc7\uff09<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%93%88_%E5%9B%BE%E8%A1%A8%E8%BE%93%E5%87%BA%EF%BC%9A\"><\/span>\ud83d\udcc8 \u56fe\u8868\u8f93\u51fa\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul>\n<li>\u6df7\u6dc6\u77e9\u9635\u70ed\u529b\u56fe\uff1a\u76f4\u89c2\u5c55\u793a TP\/FP\/TN\/FN<\/li>\n\n\n\n<li>\u652f\u6301\u540e\u7eed\u5199\u5165\u62a5\u544a\uff0c\u4fbf\u4e8e\u5411\u975e\u6280\u672f\u56e2\u961f\u6c47\u62a5<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%94%B9_%E6%A8%A1%E5%9D%974%EF%BC%9Aanalyze_feature_importance_%E2%80%94%E2%80%94_%E4%BB%8E%E6%A8%A1%E5%9E%8B%E5%88%B0%E4%B8%9A%E5%8A%A1%E6%B4%9E%E5%AF%9F%E7%9A%84%E6%A1%A5%E6%A2%81\"><\/span>\ud83d\udd39 \u6a21\u57574\uff1a<code>analyze_feature_importance()<\/code>&nbsp;\u2014\u2014&nbsp;<strong>\u4ece\u6a21\u578b\u5230\u4e1a\u52a1\u6d1e\u5bdf\u7684\u6865\u6881<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9C%85_%E5%AE%9E%E7%8E%B0%E6%96%B9%E5%BC%8F%EF%BC%9A\"><\/span>\u2705 \u5b9e\u73b0\u65b9\u5f0f\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul>\n<li>\u5bf9&nbsp;<strong>\u6811\u6a21\u578b<\/strong>\uff1a\u4f7f\u7528&nbsp;<code>feature_importances_<\/code>\uff08\u57fa\u4e8e\u4fe1\u606f\u589e\u76ca\u6216\u57fa\u5c3c\u4e0d\u7eaf\u5ea6\u4e0b\u964d\uff09<\/li>\n\n\n\n<li>\u5bf9&nbsp;<strong>\u7ebf\u6027\u6a21\u578b<\/strong>\uff1a\u4f7f\u7528&nbsp;<code>|coef_|<\/code>\uff08\u7edd\u5bf9\u7cfb\u6570\u5927\u5c0f\u8868\u793a\u5f71\u54cd\u529b\uff09<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%92%A1_%E4%B8%BA%E4%BB%80%E4%B9%88%E8%BF%99%E4%B8%80%E6%AD%A5%E6%9E%81%E5%85%B6%E9%87%8D%E8%A6%81%EF%BC%9F\"><\/span>\ud83d\udca1 \u4e3a\u4ec0\u4e48\u8fd9\u4e00\u6b65\u6781\u5176\u91cd\u8981\uff1f<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201c\u6a21\u578b\u4e0d\u4ec5\u8981\u9884\u6d4b\u51c6\uff0c\u66f4\u8981\u80fd\u8bf4\u6e05\u695a\u2018\u4e3a\u4ec0\u4e48\u2019\u3002\u201d<\/p>\n<\/blockquote>\n\n\n\n<p>\u4f8b\u5982\uff0c\u5982\u679c\u53d1\u73b0 <code>days_since_last_purchase<\/code> \u662f\u6700\u91cd\u8981\u7279\u5f81\uff0c\u8bf4\u660e\uff1a<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201c\u957f\u65f6\u95f4\u672a\u590d\u8d2d\u201d\u662f\u6d41\u5931\u6700\u5f3a\u4fe1\u53f7 \u2192 \u5e94\u5efa\u7acb <strong>\u9759\u9ed8\u7528\u6237\u5524\u9192\u673a\u5236<\/strong>\uff08\u5982\u7b2c30\u5929\u53d1\u5238\u3001\u7b2c60\u5929\u77ed\u4fe1\u63d0\u9192\uff09<\/p>\n<\/blockquote>\n\n\n\n<p>\u518d\u6bd4\u5982\uff0c\u82e5 <code>customer_service_contacts<\/code> \u6743\u91cd\u5f88\u9ad8\uff0c\u5219\u6697\u793a\uff1a<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u5ba2\u670d\u4f53\u9a8c\u5dee\u53ef\u80fd\u662f\u6d41\u5931\u4e3b\u56e0 \u2192 \u9700\u56de\u6eaf\u5de5\u5355\u5185\u5bb9\uff0c\u4f18\u5316\u670d\u52a1\u6d41\u7a0b<\/p>\n<\/blockquote>\n\n\n\n<p>\ud83d\udccc \u8fd9\u5c31\u662f <strong>AI + \u4e1a\u52a1\u8bca\u65ad<\/strong> \u7684\u878d\u5408\u70b9\uff01<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%94%B9_%E6%A8%A1%E5%9D%975%EF%BC%9Agenerate_churn_prediction_report_%E2%80%94%E2%80%94_%E8%AE%A9%E6%95%B0%E6%8D%AE%E8%AF%B4%E8%AF%9D%EF%BC%8C%E6%8E%A8%E5%8A%A8%E5%86%B3%E7%AD%96%E8%90%BD%E5%9C%B0\"><\/span>\ud83d\udd39 \u6a21\u57575\uff1a<code>generate_churn_prediction_report()<\/code>&nbsp;\u2014\u2014&nbsp;<strong>\u8ba9\u6570\u636e\u8bf4\u8bdd\uff0c\u63a8\u52a8\u51b3\u7b56\u843d\u5730<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9C%85_%E6%8A%A5%E5%91%8A%E7%BB%93%E6%9E%84%E4%B8%93%E4%B8%9A%E4%B8%94%E5%AE%9E%E7%94%A8%EF%BC%9A\"><\/span>\u2705 \u62a5\u544a\u7ed3\u6784\u4e13\u4e1a\u4e14\u5b9e\u7528\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol>\n<li><strong>\u9879\u76ee\u6982\u8ff0<\/strong>\uff1a\u660e\u786e\u76ee\u6807<\/li>\n\n\n\n<li><strong>\u6570\u636e\u6982\u89c8<\/strong>\uff1a\u589e\u5f3a\u53ef\u4fe1\u5ea6<\/li>\n\n\n\n<li><strong>\u6a21\u578b\u8868\u73b0<\/strong>\uff1a\u91cf\u5316\u6548\u679c<\/li>\n\n\n\n<li><strong>\u5173\u952e\u9a71\u52a8\u56e0\u7d20<\/strong>\uff1a\u63ed\u793a\u6839\u672c\u539f\u56e0<\/li>\n\n\n\n<li><strong>\u4e1a\u52a1\u5efa\u8bae<\/strong>\uff1a\u5c06\u7b97\u6cd5\u8f93\u51fa\u8f6c\u5316\u4e3a\u884c\u52a8\u6307\u5357<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%92%AC_%E5%85%B8%E5%9E%8B%E4%B8%9A%E5%8A%A1%E5%BB%BA%E8%AE%AE%E7%A4%BA%E4%BE%8B%EF%BC%9A\"><\/span>\ud83d\udcac \u5178\u578b\u4e1a\u52a1\u5efa\u8bae\u793a\u4f8b\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<pre class=\"wp-block-preformatted\">Text\u7f16\u8f91<code>\u5bf9\u4e8e'\u8ddd\u79bb\u4e0a\u6b21\u8d2d\u4e70\u5929\u6570'\u957f\u7684\u7528\u6237\uff0c\u53ef\u63a8\u9001\u53ec\u56de\u4f18\u60e0\u5238\u3002\n\u5bf9\u4e8e'\u8d2d\u7269\u8f66\u653e\u5f03\u7387'\u9ad8\u7684\u7528\u6237\uff0c\u53ef\u5206\u6790\u652f\u4ed8\u6d41\u7a0b\u6216\u63d0\u4f9b\u5ba2\u670d\u5e2e\u52a9\u3002<\/code><\/pre>\n\n\n\n<p>\ud83d\udc49 \u8fd9\u624d\u662f\u6570\u636e\u79d1\u5b66\u5bb6\u7684\u7ec8\u6781\u4ef7\u503c\uff1a<strong>\u4e0d\u6b62\u4e8e\u5efa\u6a21\uff0c\u800c\u5728\u4e8e\u9a71\u52a8\u589e\u957f<\/strong>\u3002<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%8E%AF_%E4%B8%89%E3%80%81%E4%BB%8E%E4%B8%9A%E5%8A%A1%E8%A7%86%E8%A7%92%E7%9C%8B%E8%BF%99%E4%B8%AA%E9%A1%B9%E7%9B%AE%E7%9A%84%E5%AE%9E%E6%88%98%E4%BB%B7%E5%80%BC\"><\/span>\ud83c\udfaf \u4e09\u3001\u4ece\u4e1a\u52a1\u89c6\u89d2\u770b\u8fd9\u4e2a\u9879\u76ee\u7684\u5b9e\u6218\u4ef7\u503c<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>\u4e1a\u52a1\u89d2\u8272<\/th><th>\u5982\u4f55\u4f7f\u7528\u6b64\u6a21\u578b\uff1f<\/th><\/tr><\/thead><tbody><tr><td><strong>\u8fd0\u8425\u7ecf\u7406<\/strong><\/td><td>\u83b7\u53d6\u9ad8\u98ce\u9669\u7528\u6237\u540d\u5355\uff0c\u5f00\u5c55\u5b9a\u5411\u53ec\u56de\u6d3b\u52a8\uff08\u90ae\u4ef6\/SMS\/APP Push\uff09<\/td><\/tr><tr><td><strong>\u4ea7\u54c1\u8d1f\u8d23\u4eba<\/strong><\/td><td>\u53d1\u73b0\u6d41\u5931\u4e3b\u56e0\uff08\u5982\u652f\u4ed8\u5931\u8d25\u7387\u9ad8\uff09\uff0c\u63a8\u52a8\u4ea7\u54c1\u8fed\u4ee3\u4f18\u5316\u6f0f\u6597<\/td><\/tr><tr><td><strong>\u5e02\u573a\u603b\u76d1<\/strong><\/td><td>\u8bc4\u4f30\u4e0d\u540c\u4eba\u7fa4\u7684LTV\uff08\u751f\u547d\u5468\u671f\u4ef7\u503c\uff09\uff0c\u8c03\u6574\u83b7\u5ba2\u9884\u7b97\u5206\u914d<\/td><\/tr><tr><td><strong>\u5ba2\u670d\u4e3b\u7ba1<\/strong><\/td><td>\u5206\u6790\u9ad8\u9891\u8054\u7cfb\u7528\u6237\u7684\u5171\u6027\u95ee\u9898\uff0c\u6539\u8fdbFAQ\u6216\u57f9\u8bad\u8bdd\u672f<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%9B%A0%EF%B8%8F_%E5%9B%9B%E3%80%81%E5%8F%AF%E6%89%A9%E5%B1%95%E6%80%A7%E4%B8%8E%E7%94%9F%E4%BA%A7%E5%8C%96%E5%BB%BA%E8%AE%AE\"><\/span>\ud83d\udee0\ufe0f \u56db\u3001\u53ef\u6269\u5c55\u6027\u4e0e\u751f\u4ea7\u5316\u5efa\u8bae<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>\u867d\u7136\u5f53\u524d\u662f\u6559\u5b66\u7ea7\u4ee3\u7801\uff0c\u4f46\u5177\u5907\u826f\u597d\u6269\u5c55\u57fa\u7840\u3002\u4ee5\u4e0b\u662f\u8fc8\u5411\u751f\u4ea7\u7684\u5347\u7ea7\u8def\u5f84\uff1a<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E2%9C%85_%E5%BD%93%E5%89%8D%E4%BC%98%E7%82%B9%EF%BC%9A\"><\/span>\u2705 \u5f53\u524d\u4f18\u70b9\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul>\n<li>\u6a21\u5757\u5316\u6e05\u6670<\/li>\n\n\n\n<li>\u5305\u542b\u81ea\u52a8\u5316\u62a5\u544a<\/li>\n\n\n\n<li>\u4f7f\u7528\u6807\u51c6\u5e93\uff08\u6613\u90e8\u7f72\uff09<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%94%A7_%E7%94%9F%E4%BA%A7%E7%8E%AF%E5%A2%83%E6%94%B9%E8%BF%9B%E5%BB%BA%E8%AE%AE%EF%BC%9A\"><\/span>\ud83d\udd27 \u751f\u4ea7\u73af\u5883\u6539\u8fdb\u5efa\u8bae\uff1a<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>\u7ef4\u5ea6<\/th><th>\u5347\u7ea7\u5efa\u8bae<\/th><\/tr><\/thead><tbody><tr><td><strong>\u6570\u636e\u6e90<\/strong><\/td><td>\u63a5\u5165 Hive\/MaxCompute\uff0c\u5b9a\u671f\u8dd1\u6279\u5904\u7406\u4efb\u52a1<\/td><\/tr><tr><td><strong>\u7279\u5f81\u5b58\u50a8<\/strong><\/td><td>\u6784\u5efa Feature Store\uff0c\u7edf\u4e00\u7ebf\u4e0a\u7ebf\u4e0b\u7279\u5f81<\/td><\/tr><tr><td><strong>\u6a21\u578b\u670d\u52a1<\/strong><\/td><td>\u7528 Flask\/FastAPI \u5c01\u88c5\u6210 API\uff0c\u4f9b\u5176\u4ed6\u7cfb\u7edf\u8c03\u7528<\/td><\/tr><tr><td><strong>\u76d1\u63a7\u62a5\u8b66<\/strong><\/td><td>\u76d1\u63a7\u6a21\u578b\u6027\u80fd\u6f02\u79fb\u3001\u7279\u5f81\u5206\u5e03\u53d8\u5316<\/td><\/tr><tr><td><strong>A\/B\u6d4b\u8bd5<\/strong><\/td><td>\u5bf9\u7167\u7ec4\u9a8c\u8bc1\u5e72\u9884\u7b56\u7565\u662f\u5426\u771f\u6b63\u964d\u4f4e\u6d41\u5931\u7387<\/td><\/tr><tr><td><strong>\u81ea\u52a8\u5316 pipeline<\/strong><\/td><td>\u4f7f\u7528 Airflow\/DolphinScheduler \u8c03\u5ea6\u6bcf\u65e5\u66f4\u65b0<\/td><\/tr><tr><td><strong>\u6a21\u578b\u53ef\u89e3\u91ca\u6027\u589e\u5f3a<\/strong><\/td><td>\u5f15\u5165 SHAP\/LIME\uff0c\u63d0\u4f9b\u4e2a\u4f53\u7ea7\u89e3\u91ca\uff08\u5982\uff1a\u201c\u60a8\u6d41\u5931\u6982\u7387\u9ad8\u7684\u539f\u56e0\u662f\u6700\u8fd160\u5929\u672a\u8d2d\u4e70\u201d\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%93%9A_%E4%BA%94%E3%80%81%E6%80%BB%E7%BB%93%EF%BC%9A%E4%B8%80%E4%B8%AA%E4%BC%98%E7%A7%80%E7%94%B5%E5%95%86%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6%E9%A1%B9%E7%9B%AE%E7%9A%84%E8%8C%83%E6%9C%AC\"><\/span>\ud83d\udcda \u4e94\u3001\u603b\u7ed3\uff1a\u4e00\u4e2a\u4f18\u79c0\u7535\u5546\u6570\u636e\u79d1\u5b66\u9879\u76ee\u7684\u8303\u672c<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>\u8fd9\u4e2a\u811a\u672c\u4f53\u73b0\u4e86\u4ee5\u4e0b\u51e0\u4e2a\u5173\u952e\u539f\u5219\uff1a<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>\u539f\u5219<\/th><th>\u5728\u672c\u9879\u76ee\u4e2d\u7684\u4f53\u73b0<\/th><\/tr><\/thead><tbody><tr><td><strong>\u4e1a\u52a1\u5bfc\u5411<\/strong><\/td><td>\u4ece\u201c\u7528\u6237\u6d41\u5931\u201d\u8fd9\u4e00\u771f\u5b9e\u75db\u70b9\u51fa\u53d1<\/td><\/tr><tr><td><strong>\u6570\u636e\u771f\u5b9e\u6027<\/strong><\/td><td>\u6a21\u62df\u6570\u636e\u9075\u5faa\u4e1a\u52a1\u89c4\u5f8b\uff0c\u975e\u968f\u610f\u751f\u6210<\/td><\/tr><tr><td><strong>\u65b9\u6cd5\u4e25\u8c28\u6027<\/strong><\/td><td>\u6b63\u786e\u4f7f\u7528\u7edf\u8ba1\u4e0e\u673a\u5668\u5b66\u4e60\u65b9\u6cd5<\/td><\/tr><tr><td><strong>\u7ed3\u679c\u53ef\u89e3\u91ca<\/strong><\/td><td>\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790\u8fde\u63a5\u6a21\u578b\u4e0e\u4e1a\u52a1<\/td><\/tr><tr><td><strong>\u4ea4\u4ed8\u5b9e\u7528\u6027<\/strong><\/td><td>\u81ea\u52a8\u751f\u6210\u56fe\u6587\u62a5\u544a\uff0c\u652f\u6301\u51b3\u7b56<\/td><\/tr><tr><td><strong>\u5de5\u7a0b\u89c4\u8303\u6027<\/strong><\/td><td>\u51fd\u6570\u5316\u7ec4\u7ec7\uff0c\u6ce8\u91ca\u6e05\u6670\uff0c\u6613\u4e8e\u7ef4\u62a4<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%F0%9F%8E%81_%E7%BB%93%E8%AF%AD\"><\/span>\ud83c\udf81 \u7ed3\u8bed<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201c\u6700\u597d\u7684\u6a21\u578b\u4e0d\u662f\u51c6\u786e\u7387\u6700\u9ad8\u7684\u90a3\u4e2a\uff0c\u800c\u662f\u80fd\u8ba9\u4e1a\u52a1\u56e2\u961f\u542c\u61c2\u5e76\u613f\u610f\u884c\u52a8\u7684\u90a3\u4e2a\u3002\u201d<\/p>\n<\/blockquote>\n\n\n\n<p>\u8fd9\u5957\u4ee3\u7801\u4e0d\u4ec5\u5c55\u793a\u4e86 <strong>Python \u6570\u636e\u6316\u6398\u7684\u6280\u672f\u5b9e\u73b0<\/strong>\uff0c\u66f4\u4f53\u73b0\u4e86 <strong>\u8d44\u6df1\u6570\u636e\u4e13\u5bb6\u7684\u601d\u7ef4\u6846\u67b6<\/strong><\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime, timedelta\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# --- \u914d\u7f6e ---\nNUM_USERS = 5000\nREPORT_PREFIX = '\u7535\u5546\u7528\u6237\u6d41\u5931\u9884\u6d4b\u62a5\u544a'\nRANDOM_SEED = 42\n\n# --- \u6570\u636e\u751f\u6210 ---\n\ndef generate_sample_churn_data(n_users):\n    \"\"\"\u751f\u6210\u6a21\u62df\u7684\u7528\u6237\u6d41\u5931\u6570\u636e\"\"\"\n    print(\"--- \u6b63\u5728\u751f\u6210\u6a21\u62df\u7528\u6237\u6570\u636e ---\")\n    np.random.seed(RANDOM_SEED)\n    \n    data = &#91;]\n    user_ids = &#91;f'user_{i}' for i in range(1, n_users + 1)]\n    \n    for user_id in user_ids:\n        # --- \u7528\u6237\u57fa\u7840\u753b\u50cf ---\n        age = np.random.randint(18, 65)\n        gender = np.random.choice(&#91;'Male', 'Female'], p=&#91;0.5, 0.5])\n        membership_tier = np.random.choice(&#91;'Bronze', 'Silver', 'Gold'], p=&#91;0.6, 0.3, 0.1])\n        location_city_tier = np.random.choice(&#91;'Tier_1', 'Tier_2', 'Tier_3'], p=&#91;0.3, 0.4, 0.3])\n        \n        # --- \u7528\u6237\u884c\u4e3a\u7279\u5f81 ---\n        # \u6ce8\u518c\u65e5\u671f (\u5047\u8bbe\u57282\u5e74\u524d\u52301\u5e74\u524d\u4e4b\u95f4)\n        signup_date = datetime.now() - timedelta(days=np.random.randint(365, 2*365))\n        \n        # \u603b\u8ba2\u5355\u6570\u548c\u603b\u6d88\u8d39 (\u4e0e\u4f1a\u5458\u7b49\u7ea7\u76f8\u5173)\n        if membership_tier == 'Gold':\n            total_orders = np.random.poisson(20)\n            total_spent = np.random.lognormal(10.5, 0.4)\n        elif membership_tier == 'Silver':\n            total_orders = np.random.poisson(10)\n            total_spent = np.random.lognormal(9.5, 0.5)\n        else: # Bronze\n            total_orders = np.random.poisson(5)\n            total_spent = np.random.lognormal(8.5, 0.6)\n            \n        # \u5e73\u5747\u8ba2\u5355\u4ef7\u503c\n        avg_order_value = total_spent \/ max(total_orders, 1) \n        \n        # \u6700\u8fd1\u4e00\u6b21\u8d2d\u4e70\u8ddd\u79bb\u5929\u6570 (\u5173\u952e\u7279\u5f81)\n        # \u5047\u8bbeGold\u7528\u6237\u66f4\u6d3b\u8dc3\uff0c\u6d41\u5931\u98ce\u9669\u4f4e\uff1bBronze\u7528\u6237\u6d41\u5931\u98ce\u9669\u9ad8\n        if membership_tier == 'Gold':\n            days_since_last_purchase = np.random.exponential(15) # \u5e73\u574715\u5929\n        elif membership_tier == 'Silver':\n            days_since_last_purchase = np.random.exponential(30) # \u5e73\u574730\u5929\n        else: # Bronze\n            days_since_last_purchase = np.random.exponential(60) # \u5e73\u574760\u5929\n            \n        last_purchase_date = datetime.now() - timedelta(days=days_since_last_purchase)\n        \n        # \u6708\u5747\u8bbf\u95ee\u6b21\u6570 (\u6d3b\u8dc3\u5ea6)\n        if membership_tier == 'Gold':\n            monthly_visits = np.random.poisson(15)\n        elif membership_tier == 'Silver':\n            monthly_visits = np.random.poisson(10)\n        else: # Bronze\n            monthly_visits = np.random.poisson(5)\n            \n        # \u8d2d\u7269\u8f66\u653e\u5f03\u7387 (\u52a0\u8d2d\u4f46\u672a\u8d2d\u4e70\u7684\u8ba2\u5355 \/ \u603b\u52a0\u8d2d\u6b21\u6570)\n        cart_abandonment_rate = np.random.beta(2, 5) # \u5927\u591a\u6570\u7528\u6237\u653e\u5f03\u7387\u8f83\u4f4e\n        \n        # \u5ba2\u670d\u8054\u7cfb\u6b21\u6570 (\u6700\u8fd1\u4e00\u5e74)\n        customer_service_contacts = np.random.poisson(2)\n        \n        # \u6298\u6263\u4f7f\u7528\u9891\u7387\n        discount_usage_freq = np.random.beta(3, 7) # \u5927\u591a\u6570\u7528\u6237\u4e0d\u5e38\u7528\u6298\u6263\n        \n        # --- \u6784\u9020\u6d41\u5931\u6807\u7b7e (\u57fa\u4e8e\u7279\u5f81\u7684\u6982\u7387) ---\n        # \u8fd9\u662f\u4e00\u4e2a\u7b80\u5316\u7684\u6a21\u62df\u903b\u8f91\uff0c\u771f\u5b9e\u573a\u666f\u4f1a\u66f4\u590d\u6742\n        churn_prob = 0.0\n        \n        # \u957f\u65f6\u95f4\u672a\u8d2d\u4e70\u662f\u5f3a\u4fe1\u53f7\n        if days_since_last_purchase &gt; 90:\n            churn_prob += 0.4\n        elif days_since_last_purchase &gt; 60:\n            churn_prob += 0.2\n        elif days_since_last_purchase &gt; 30:\n            churn_prob += 0.1\n            \n        # \u8d2d\u7269\u8f66\u653e\u5f03\u7387\u9ad8\u53ef\u80fd\u8868\u793a\u72b9\u8c6b\u6216\u4e0d\u6ee1\n        if cart_abandonment_rate &gt; 0.7:\n            churn_prob += 0.15\n            \n        # \u5ba2\u670d\u8054\u7cfb\u591a\u53ef\u80fd\u8868\u793a\u6709\u95ee\u9898\n        if customer_service_contacts &gt; 5:\n            churn_prob += 0.1\n            \n        # \u975e\u6d3b\u8dc3\u7528\u6237 (\u6708\u8bbf\u95ee\u6b21\u6570\u5c11)\n        if monthly_visits &lt; 3:\n            churn_prob += 0.1\n            \n        # Bronze\u4f1a\u5458\u672c\u8eab\u6d41\u5931\u7387\u53ef\u80fd\u7a0d\u9ad8\n        if membership_tier == 'Bronze':\n            churn_prob += 0.05\n            \n        # \u52a0\u5165\u968f\u673a\u6027\n        churn_prob += np.random.normal(0, 0.1)\n        churn_prob = np.clip(churn_prob, 0, 1) # \u9650\u5236\u57280-1\u4e4b\u95f4\n        \n        is_churned = int(np.random.random() &lt; churn_prob)\n        \n        data.append({\n            'user_id': user_id,\n            'age': age,\n            'gender': gender,\n            'membership_tier': membership_tier,\n            'location_city_tier': location_city_tier,\n            'signup_date': signup_date,\n            'total_orders': total_orders,\n            'total_spent': round(total_spent, 2),\n            'avg_order_value': round(avg_order_value, 2),\n            'days_since_last_purchase': round(days_since_last_purchase, 2),\n            'monthly_visits': monthly_visits,\n            'cart_abandonment_rate': round(cart_abandonment_rate, 4),\n            'customer_service_contacts': customer_service_contacts,\n            'discount_usage_freq': round(discount_usage_freq, 4),\n            'is_churned': is_churned\n        })\n        \n    df = pd.DataFrame(data)\n    # \u8ba1\u7b97\u884d\u751f\u7279\u5f81\n    df&#91;'signup_date'] = pd.to_datetime(df&#91;'signup_date'])\n    df&#91;'tenure_days'] = (datetime.now() - df&#91;'signup_date']).dt.days\n    df&#91;'tenure_days'] = df&#91;'tenure_days'].astype(int)\n    \n    csv_filename = f'{REPORT_PREFIX}_\u6a21\u62df\u6570\u636e.csv'\n    df.to_csv(csv_filename, index=False, encoding='utf-8-sig')\n    print(f\"\u6a21\u62df\u6570\u636e\u5df2\u751f\u6210\u5e76\u4fdd\u5b58\u81f3: {csv_filename}\")\n    return df\n\n# --- \u6570\u636e\u9884\u5904\u7406 ---\n\ndef preprocess_data(df):\n    \"\"\"\u6570\u636e\u9884\u5904\u7406\"\"\"\n    print(\"\\n--- \u6b63\u5728\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406 ---\")\n    df_processed = df.copy()\n    \n    # 1. \u5904\u7406\u65e5\u671f\u7279\u5f81 (\u8fd9\u91cc\u5df2\u8f6c\u6362\u4e3a\u5929\u6570\uff0c\u65e0\u9700\u8fdb\u4e00\u6b65\u5904\u7406)\n    \n    # 2. \u7f16\u7801\u5206\u7c7b\u53d8\u91cf\n    le_gender = LabelEncoder()\n    le_membership = LabelEncoder()\n    le_location = LabelEncoder()\n    \n    df_processed&#91;'gender_encoded'] = le_gender.fit_transform(df_processed&#91;'gender'])\n    df_processed&#91;'membership_tier_encoded'] = le_membership.fit_transform(df_processed&#91;'membership_tier'])\n    df_processed&#91;'location_city_tier_encoded'] = le_location.fit_transform(df_processed&#91;'location_city_tier'])\n    \n    # 3. \u9009\u62e9\u7528\u4e8e\u5efa\u6a21\u7684\u7279\u5f81\u5217\n    feature_columns = &#91;\n        'age', 'gender_encoded', 'membership_tier_encoded', 'location_city_tier_encoded',\n        'total_orders', 'total_spent', 'avg_order_value', 'days_since_last_purchase',\n        'monthly_visits', 'cart_abandonment_rate', 'customer_service_contacts',\n        'discount_usage_freq', 'tenure_days'\n    ]\n    \n    X = df_processed&#91;feature_columns]\n    y = df_processed&#91;'is_churned']\n    \n    print(f\"\u9884\u5904\u7406\u5b8c\u6210\u3002\u7279\u5f81\u77e9\u9635\u5f62\u72b6: {X.shape}, \u6807\u7b7e\u5411\u91cf\u5f62\u72b6: {y.shape}\")\n    return X, y, le_gender, le_membership, le_location, feature_columns\n\n# --- \u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30 ---\n\ndef train_and_evaluate_models(X, y):\n    \"\"\"\u8bad\u7ec3\u548c\u8bc4\u4f30\u591a\u4e2a\u6a21\u578b\"\"\"\n    print(\"\\n--- \u6b63\u5728\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b ---\")\n    \n    # \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RANDOM_SEED, stratify=y)\n    \n    # \u7279\u5f81\u7f29\u653e (\u5bf9\u903b\u8f91\u56de\u5f52\u6bd4\u8f83\u91cd\u8981)\n    scaler = StandardScaler()\n    X_train_scaled = scaler.fit_transform(X_train)\n    X_test_scaled = scaler.transform(X_test)\n    \n    results = {}\n    \n    # --- 1. \u903b\u8f91\u56de\u5f52 ---\n    print(\"\u8bad\u7ec3\u903b\u8f91\u56de\u5f52\u6a21\u578b...\")\n    model_lr = LogisticRegression(random_state=RANDOM_SEED, max_iter=1000)\n    model_lr.fit(X_train_scaled, y_train)\n    y_pred_lr = model_lr.predict(X_test_scaled)\n    y_pred_proba_lr = model_lr.predict_proba(X_test_scaled)&#91;:, 1]\n    \n    results&#91;'Logistic Regression'] = {\n        'model': model_lr,\n        'predictions': y_pred_lr,\n        'probabilities': y_pred_proba_lr,\n        'accuracy': accuracy_score(y_test, y_pred_lr),\n        'precision': precision_score(y_test, y_pred_lr),\n        'recall': recall_score(y_test, y_pred_lr),\n        'f1': f1_score(y_test, y_pred_lr),\n        'auc': roc_auc_score(y_test, y_pred_proba_lr)\n    }\n    \n    # --- 2. \u968f\u673a\u68ee\u6797 ---\n    print(\"\u8bad\u7ec3\u968f\u673a\u68ee\u6797\u6a21\u578b...\")\n    model_rf = RandomForestClassifier(n_estimators=100, random_state=RANDOM_SEED, n_jobs=-1)\n    model_rf.fit(X_train, y_train) # \u968f\u673a\u68ee\u6797\u4e0d\u9700\u8981\u7279\u5f81\u7f29\u653e\n    y_pred_rf = model_rf.predict(X_test)\n    y_pred_proba_rf = model_rf.predict_proba(X_test)&#91;:, 1]\n    \n    results&#91;'Random Forest'] = {\n        'model': model_rf,\n        'predictions': y_pred_rf,\n        'probabilities': y_pred_proba_rf,\n        'accuracy': accuracy_score(y_test, y_pred_rf),\n        'precision': precision_score(y_test, y_pred_rf),\n        'recall': recall_score(y_test, y_pred_rf),\n        'f1': f1_score(y_test, y_pred_rf),\n        'auc': roc_auc_score(y_test, y_pred_proba_rf)\n    }\n    \n    # --- \u6bd4\u8f83\u548c\u62a5\u544a ---\n    print(\"\\n--- \u6a21\u578b\u6027\u80fd\u5bf9\u6bd4 ---\")\n    comparison_df = pd.DataFrame({\n        'Model': list(results.keys()),\n        'Accuracy': &#91;results&#91;k]&#91;'accuracy'] for k in results.keys()],\n        'Precision': &#91;results&#91;k]&#91;'precision'] for k in results.keys()],\n        'Recall': &#91;results&#91;k]&#91;'recall'] for k in results.keys()],\n        'F1-Score': &#91;results&#91;k]&#91;'f1'] for k in results.keys()],\n        'AUC-ROC': &#91;results&#91;k]&#91;'auc'] for k in results.keys()]\n    })\n    print(comparison_df.round(4).to_string(index=False))\n    \n    # \u9009\u62e9\u6700\u4f73\u6a21\u578b (\u4ee5AUC\u4e3a\u51c6)\n    best_model_name = comparison_df.loc&#91;comparison_df&#91;'AUC-ROC'].idxmax(), 'Model']\n    best_model = results&#91;best_model_name]&#91;'model']\n    best_predictions = results&#91;best_model_name]&#91;'predictions']\n    best_probabilities = results&#91;best_model_name]&#91;'probabilities']\n    \n    print(f\"\\n\u9009\u62e9\u6700\u4f73\u6a21\u578b: {best_model_name}\")\n    \n    # \u7ed8\u5236\u6700\u4f73\u6a21\u578b\u7684\u6df7\u6dc6\u77e9\u9635\n    cm = confusion_matrix(y_test, best_predictions)\n    plt.figure(figsize=(8, 6))\n    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n                xticklabels=&#91;'Not Churned', 'Churned'], \n                yticklabels=&#91;'Not Churned', 'Churned'])\n    plt.title(f'\u6df7\u6dc6\u77e9\u9635 - {best_model_name}')\n    plt.xlabel('\u9884\u6d4b\u6807\u7b7e')\n    plt.ylabel('\u771f\u5b9e\u6807\u7b7e')\n    cm_path = f'{REPORT_PREFIX}_\u6df7\u6dc6\u77e9\u9635_{best_model_name.replace(\" \", \"_\")}.png'\n    plt.savefig(cm_path)\n    plt.close()\n    print(f\"\u6df7\u6dc6\u77e9\u9635\u56fe\u8868\u5df2\u4fdd\u5b58\u81f3: {cm_path}\")\n    \n    # \u6253\u5370\u6700\u4f73\u6a21\u578b\u7684\u8be6\u7ec6\u5206\u7c7b\u62a5\u544a\n    print(f\"\\n--- {best_model_name} \u8be6\u7ec6\u5206\u7c7b\u62a5\u544a ---\")\n    print(classification_report(y_test, best_predictions, target_names=&#91;'Not Churned', 'Churned']))\n    \n    return results, best_model_name, scaler, X_test, y_test, cm_path\n\n# --- \u7279\u5f81\u91cd\u8981\u6027\u5206\u6790 ---\n\ndef analyze_feature_importance(model, feature_names, model_name):\n    \"\"\"\u5206\u6790\u5e76\u53ef\u89c6\u5316\u7279\u5f81\u91cd\u8981\u6027\"\"\"\n    print(f\"\\n--- \u5206\u6790 {model_name} \u7279\u5f81\u91cd\u8981\u6027 ---\")\n    \n    if hasattr(model, 'feature_importances_'):\n        # \u968f\u673a\u68ee\u6797\u7b49\u6811\u6a21\u578b\n        importances = model.feature_importances_\n        indices = np.argsort(importances)&#91;::-1]\n        title = f'{model_name} - \u7279\u5f81\u91cd\u8981\u6027 (\u57fa\u4e8e\u4e0d\u7eaf\u5ea6)'\n    elif hasattr(model, 'coef_'):\n        # \u7ebf\u6027\u6a21\u578b (\u903b\u8f91\u56de\u5f52\u7cfb\u6570\u7684\u7edd\u5bf9\u503c)\n        importances = np.abs(model.coef_&#91;0])\n        indices = np.argsort(importances)&#91;::-1]\n        title = f'{model_name} - \u7279\u5f81\u91cd\u8981\u6027 (\u57fa\u4e8e\u7cfb\u6570\u7edd\u5bf9\u503c)'\n    else:\n        print(\"\u6a21\u578b\u4e0d\u652f\u6301\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790\u3002\")\n        return None, None\n\n    # \u521b\u5efa\u7279\u5f81\u91cd\u8981\u6027DataFrame\n    feature_importance_df = pd.DataFrame({\n        'feature': &#91;feature_names&#91;i] for i in indices],\n        'importance': &#91;importances&#91;i] for i in indices]\n    })\n    \n    print(\"\u7279\u5f81\u91cd\u8981\u6027\u6392\u5e8f:\")\n    print(feature_importance_df.head(10).to_string(index=False))\n\n    # \u7ed8\u5236\u7279\u5f81\u91cd\u8981\u6027\n    plt.figure(figsize=(10, 6))\n    sns.barplot(data=feature_importance_df.head(10), x='importance', y='feature', palette='viridis')\n    plt.title(title)\n    plt.xlabel('\u91cd\u8981\u6027')\n    plt.tight_layout()\n    feat_imp_path = f'{REPORT_PREFIX}_\u7279\u5f81\u91cd\u8981\u6027_{model_name.replace(\" \", \"_\")}.png'\n    plt.savefig(feat_imp_path)\n    plt.close()\n    print(f\"\u7279\u5f81\u91cd\u8981\u6027\u56fe\u8868\u5df2\u4fdd\u5b58\u81f3: {feat_imp_path}\")\n    \n    return feature_importance_df, feat_imp_path\n\n# --- \u62a5\u544a\u751f\u6210 ---\n\ndef generate_churn_prediction_report(best_model_name, results, cm_path, feat_imp_df, feat_imp_path):\n    \"\"\"\u751f\u6210\u6700\u7ec8\u7684\u6d41\u5931\u9884\u6d4b\u5206\u6790\u62a5\u544a\"\"\"\n    print(\"\\n--- \u6b63\u5728\u751f\u6210\u6d41\u5931\u9884\u6d4b\u5206\u6790\u62a5\u544a ---\")\n    from datetime import datetime\n    report_filename = f\"{REPORT_PREFIX}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt\"\n    \n    best_metrics = results&#91;best_model_name]\n    \n    with open(report_filename, 'w', encoding='utf-8') as f:\n        f.write(\"=\" * 50 + \"\\n\")\n        f.write(\"        \u7535\u5546\u5e73\u53f0\u7528\u6237\u6d41\u5931\u9884\u6d4b\u5206\u6790\u62a5\u544a\\n\")\n        f.write(f\"        \u751f\u6210\u65f6\u95f4: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\\n\")\n        f.write(\"=\" * 50 + \"\\n\\n\")\n\n        f.write(\"--- 1. \u9879\u76ee\u6982\u8ff0 ---\\n\")\n        f.write(\"\u672c\u9879\u76ee\u65e8\u5728\u901a\u8fc7\u5206\u6790\u7528\u6237\u5386\u53f2\u884c\u4e3a\u6570\u636e\uff0c\u6784\u5efa\u673a\u5668\u5b66\u4e60\u6a21\u578b\u6765\u9884\u6d4b\u7528\u6237\u6d41\u5931\u7684\u53ef\u80fd\u6027\u3002\\n\")\n        f.write(\"\u76ee\u6807\u662f\u8bc6\u522b\u51fa\u9ad8\u98ce\u9669\u7528\u6237\uff0c\u4ee5\u4fbf\u8fd0\u8425\u56e2\u961f\u53ef\u4ee5\u63d0\u524d\u91c7\u53d6\u5e72\u9884\u63aa\u65bd\uff0c\u964d\u4f4e\u7528\u6237\u6d41\u5931\u7387\u3002\\n\\n\")\n\n        f.write(\"--- 2. \u6570\u636e\u6982\u89c8 ---\\n\")\n        f.write(\"\u6570\u636e\u6765\u6e90: \u6a21\u62df\u751f\u6210\u7684\u7535\u5546\u5e73\u53f0\u7528\u6237\u6570\u636e\u3002\\n\")\n        f.write(\"\u5173\u952e\u5b57\u6bb5: \u7528\u6237ID, \u5e74\u9f84, \u6027\u522b, \u4f1a\u5458\u7b49\u7ea7, \u57ce\u5e02\u7ea7\u522b, \u603b\u8ba2\u5355\u6570, \u603b\u6d88\u8d39\u989d, \u5e73\u5747\u8ba2\u5355\u4ef7\u503c,\\n\")\n        f.write(\"          \u8ddd\u79bb\u4e0a\u6b21\u8d2d\u4e70\u5929\u6570, \u6708\u5747\u8bbf\u95ee\u6b21\u6570, \u8d2d\u7269\u8f66\u653e\u5f03\u7387, \u5ba2\u670d\u8054\u7cfb\u6b21\u6570, \u6298\u6263\u4f7f\u7528\u9891\u7387, \u6ce8\u518c\u65f6\u957f, \u6d41\u5931\u6807\u7b7e\u3002\\n\")\n        f.write(\"\u539f\u59cb\u6570\u636e\u5df2\u4fdd\u5b58\u4e3a CSV \u6587\u4ef6\u3002\\n\\n\")\n\n        f.write(\"--- 3. \u6a21\u578b\u6027\u80fd\u8bc4\u4f30 ---\\n\")\n        f.write(\"\u8bad\u7ec3\u4e86\u4e24\u79cd\u6a21\u578b\uff1a\u903b\u8f91\u56de\u5f52 (Logistic Regression) \u548c \u968f\u673a\u68ee\u6797 (Random Forest)\u3002\\n\")\n        f.write(\"\u8bc4\u4f30\u6307\u6807\u5305\u62ec: \u51c6\u786e\u7387 (Accuracy), \u7cbe\u786e\u7387 (Precision), \u53ec\u56de\u7387 (Recall), F1\u5206\u6570 (F1-Score), AUC-ROC\u3002\\n\\n\")\n        \n        f.write(\"\u5404\u6a21\u578b\u6027\u80fd\u5bf9\u6bd4:\\n\")\n        comparison_df = pd.DataFrame({\n            'Model': list(results.keys()),\n            'Accuracy': &#91;results&#91;k]&#91;'accuracy'] for k in results.keys()],\n            'Precision': &#91;results&#91;k]&#91;'precision'] for k in results.keys()],\n            'Recall': &#91;results&#91;k]&#91;'recall'] for k in results.keys()],\n            'F1-Score': &#91;results&#91;k]&#91;'f1'] for k in results.keys()],\n            'AUC-ROC': &#91;results&#91;k]&#91;'auc'] for k in results.keys()]\n        })\n        f.write(comparison_df.round(4).to_string(index=False))\n        f.write(\"\\n\\n\")\n        \n        f.write(f\"\u6700\u4f73\u6a21\u578b: {best_model_name}\\n\")\n        f.write(f\"\u8be5\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8868\u73b0:\\n\")\n        f.write(f\"  - \u51c6\u786e\u7387 (Accuracy): {best_metrics&#91;'accuracy']:.4f}\\n\")\n        f.write(f\"  - \u7cbe\u786e\u7387 (Precision): {best_metrics&#91;'precision']:.4f}\\n\")\n        f.write(f\"  - \u53ec\u56de\u7387 (Recall): {best_metrics&#91;'recall']:.4f}\\n\")\n        f.write(f\"  - F1\u5206\u6570 (F1-Score): {best_metrics&#91;'f1']:.4f}\\n\")\n        f.write(f\"  - AUC-ROC: {best_metrics&#91;'auc']:.4f}\\n\")\n        f.write(\"\u6df7\u6dc6\u77e9\u9635\u8be6\u7ec6\u5206\u6790\u4e86\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\uff0c\u56fe\u8868\u5df2\u751f\u6210\u3002\\n\")\n        f.write(f\"\u6df7\u6dc6\u77e9\u9635\u56fe\u8868: {cm_path}\\n\\n\")\n\n        f.write(\"--- 4. \u5173\u952e\u9a71\u52a8\u56e0\u7d20\u5206\u6790 ---\\n\")\n        f.write(\"\u901a\u8fc7\u5206\u6790\u6700\u4f73\u6a21\u578b\u7684\u7279\u5f81\u91cd\u8981\u6027\uff0c\u8bc6\u522b\u51fa\u5f71\u54cd\u7528\u6237\u6d41\u5931\u7684\u6700\u5173\u952e\u56e0\u7d20\u3002\\n\")\n        f.write(\"\u7279\u5f81\u91cd\u8981\u6027\u6392\u5e8f (Top 10):\\n\")\n        f.write(feat_imp_df.head(10).to_string(index=False))\n        f.write(\"\\n\u4ece\u4e0a\u8868\u53ef\u4ee5\u770b\u51fa\uff0c\u54ea\u4e9b\u7528\u6237\u884c\u4e3a\u548c\u5c5e\u6027\u5bf9\u6d41\u5931\u5f71\u54cd\u6700\u5927\u3002\\n\")\n        f.write(f\"\u7279\u5f81\u91cd\u8981\u6027\u56fe\u8868: {feat_imp_path}\\n\\n\")\n\n        f.write(\"--- 5. \u4e1a\u52a1\u5e94\u7528\u4e0e\u5efa\u8bae ---\\n\")\n        f.write(\"1. \u9ad8\u98ce\u9669\u7528\u6237\u8bc6\u522b: \u5229\u7528\u6700\u4f73\u6a21\u578b\u5bf9\u6240\u6709\u7528\u6237\u8ba1\u7b97\u6d41\u5931\u6982\u7387\uff0c\u7b5b\u9009\u51fa\u9ad8\u98ce\u9669\u7528\u6237\u5217\u8868\u3002\\n\")\n        f.write(\"2. \u7cbe\u51c6\u5e72\u9884:\\n\")\n        f.write(\"   - \u5bf9\u4e8e'\u8ddd\u79bb\u4e0a\u6b21\u8d2d\u4e70\u5929\u6570'\u957f\u7684\u7528\u6237\uff0c\u53ef\u63a8\u9001\u53ec\u56de\u4f18\u60e0\u5238\u3002\\n\")\n        f.write(\"   - \u5bf9\u4e8e'\u8d2d\u7269\u8f66\u653e\u5f03\u7387'\u9ad8\u7684\u7528\u6237\uff0c\u53ef\u5206\u6790\u652f\u4ed8\u6d41\u7a0b\u6216\u63d0\u4f9b\u5ba2\u670d\u5e2e\u52a9\u3002\\n\")\n        f.write(\"   - \u5bf9\u4e8e'\u6708\u5747\u8bbf\u95ee\u6b21\u6570'\u5c11\u7684\u7528\u6237\uff0c\u53ef\u901a\u8fc7\u90ae\u4ef6\/SMS\u63a8\u9001\u4e2a\u6027\u5316\u5185\u5bb9\u3002\\n\")\n        f.write(\"3. \u4ea7\u54c1\u4e0e\u8fd0\u8425\u4f18\u5316:\\n\")\n        f.write(\"   - \u6839\u636e\u5173\u952e\u7279\u5f81\u4f18\u5316\u7528\u6237\u5f15\u5bfc\u548c\u7559\u5b58\u7b56\u7565\u3002\\n\")\n        f.write(\"   - \u9488\u5bf9\u4e0d\u540c\u4f1a\u5458\u7b49\u7ea7\u5236\u5b9a\u5dee\u5f02\u5316\u7684\u5fe0\u8bda\u5ea6\u8ba1\u5212\u3002\\n\")\n        f.write(\"4. \u6a21\u578b\u8fed\u4ee3: \u5b9a\u671f\u4f7f\u7528\u6700\u65b0\u6570\u636e\u91cd\u65b0\u8bad\u7ec3\u6a21\u578b\uff0c\u4ee5\u9002\u5e94\u5e02\u573a\u548c\u7528\u6237\u884c\u4e3a\u7684\u53d8\u5316\u3002\\n\\n\")\n\n        f.write(\"=\" * 50 + \"\\n\")\n        f.write(\"                    \u62a5\u544a\u7ed3\u675f\\n\")\n        f.write(\"=\" * 50 + \"\\n\")\n\n    print(f\"\u6d41\u5931\u9884\u6d4b\u5206\u6790\u62a5\u544a\u5df2\u751f\u6210: {report_filename}\")\n\n# --- \u4e3b\u51fd\u6570 ---\n\ndef main():\n    \"\"\"\u4e3b\u51fd\u6570\"\"\"\n    # 1. \u751f\u6210\u6570\u636e\n    df_churn = generate_sample_churn_data(NUM_USERS)\n    \n    # 2. \u6570\u636e\u9884\u5904\u7406\n    X, y, le_gender, le_membership, le_location, feature_cols = preprocess_data(df_churn)\n    \n    # 3. \u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30\n    results, best_model_name, scaler, X_test, y_test, cm_path = train_and_evaluate_models(X, y)\n    \n    # 4. \u7279\u5f81\u91cd\u8981\u6027\u5206\u6790 (\u9488\u5bf9\u6700\u4f73\u6a21\u578b)\n    best_model = results&#91;best_model_name]&#91;'model']\n    feat_imp_df, feat_imp_path = analyze_feature_importance(best_model, feature_cols, best_model_name)\n    \n    # 5. \u751f\u6210\u62a5\u544a\n    generate_churn_prediction_report(best_model_name, results, cm_path, feat_imp_df, feat_imp_path)\n    \n    print(\"\\n\u7528\u6237\u6d41\u5931\u9884\u6d4b\u5206\u6790\u6d41\u7a0b\u5b8c\u6210\u3002\")\n\nif __name__ == \"__main__\":\n    main()<\/code><\/pre>\n\n\n\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>==================================================<br>\u7535\u5546\u5e73\u53f0\u7528\u6237\u6d41\u5931\u9884\u6d4b\u5206\u6790\u62a5\u544a<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E7%94%9F%E6%88%90%E6%97%B6%E9%97%B4_2025-10-12_22_49_22\"><\/span>\u751f\u6210\u65f6\u95f4: 2025-10-12 22:49:22<span class=\"ez-toc-section-end\"><\/span><\/h1>\n\n\n\n<p>&#8212; 1. \u9879\u76ee\u6982\u8ff0 &#8212;<br>\u672c\u9879\u76ee\u65e8\u5728\u901a\u8fc7\u5206\u6790\u7528\u6237\u5386\u53f2\u884c\u4e3a\u6570\u636e\uff0c\u6784\u5efa\u673a\u5668\u5b66\u4e60\u6a21\u578b\u6765\u9884\u6d4b\u7528\u6237\u6d41\u5931\u7684\u53ef\u80fd\u6027\u3002<br>\u76ee\u6807\u662f\u8bc6\u522b\u51fa\u9ad8\u98ce\u9669\u7528\u6237\uff0c\u4ee5\u4fbf\u8fd0\u8425\u56e2\u961f\u53ef\u4ee5\u63d0\u524d\u91c7\u53d6\u5e72\u9884\u63aa\u65bd\uff0c\u964d\u4f4e\u7528\u6237\u6d41\u5931\u7387\u3002<\/p>\n\n\n\n<p>&#8212; 2. \u6570\u636e\u6982\u89c8 &#8212;<br>\u6570\u636e\u6765\u6e90: \u6a21\u62df\u751f\u6210\u7684\u7535\u5546\u5e73\u53f0\u7528\u6237\u6570\u636e\u3002<br>\u5173\u952e\u5b57\u6bb5: \u7528\u6237ID, \u5e74\u9f84, \u6027\u522b, \u4f1a\u5458\u7b49\u7ea7, \u57ce\u5e02\u7ea7\u522b, \u603b\u8ba2\u5355\u6570, \u603b\u6d88\u8d39\u989d, \u5e73\u5747\u8ba2\u5355\u4ef7\u503c,<br>\u8ddd\u79bb\u4e0a\u6b21\u8d2d\u4e70\u5929\u6570, \u6708\u5747\u8bbf\u95ee\u6b21\u6570, \u8d2d\u7269\u8f66\u653e\u5f03\u7387, \u5ba2\u670d\u8054\u7cfb\u6b21\u6570, \u6298\u6263\u4f7f\u7528\u9891\u7387, \u6ce8\u518c\u65f6\u957f, \u6d41\u5931\u6807\u7b7e\u3002<br>\u539f\u59cb\u6570\u636e\u5df2\u4fdd\u5b58\u4e3a CSV \u6587\u4ef6\u3002<\/p>\n\n\n\n<p>&#8212; 3. \u6a21\u578b\u6027\u80fd\u8bc4\u4f30 &#8212;<br>\u8bad\u7ec3\u4e86\u4e24\u79cd\u6a21\u578b\uff1a\u903b\u8f91\u56de\u5f52 (Logistic Regression) \u548c \u968f\u673a\u68ee\u6797 (Random Forest)\u3002<br>\u8bc4\u4f30\u6307\u6807\u5305\u62ec: \u51c6\u786e\u7387 (Accuracy), \u7cbe\u786e\u7387 (Precision), \u53ec\u56de\u7387 (Recall), F1\u5206\u6570 (F1-Score), AUC-ROC\u3002<\/p>\n\n\n\n<p>\u5404\u6a21\u578b\u6027\u80fd\u5bf9\u6bd4:<br>Model Accuracy Precision Recall F1-Score AUC-ROC<br>Logistic Regression 0.815 0.2895 0.0651 0.1063 0.7265<br>Random Forest 0.830 0.4923 0.1893 0.2735 0.7275<\/p>\n\n\n\n<p>\u6700\u4f73\u6a21\u578b: Random Forest<br>\u8be5\u6a21\u578b\u5728\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8868\u73b0:<\/p>\n\n\n\n<ul>\n<li>\u51c6\u786e\u7387 (Accuracy): 0.8300<\/li>\n\n\n\n<li>\u7cbe\u786e\u7387 (Precision): 0.4923<\/li>\n\n\n\n<li>\u53ec\u56de\u7387 (Recall): 0.1893<\/li>\n\n\n\n<li>F1\u5206\u6570 (F1-Score): 0.2735<\/li>\n\n\n\n<li>AUC-ROC: 0.7275<br>\u6df7\u6dc6\u77e9\u9635\u8be6\u7ec6\u5206\u6790\u4e86\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\uff0c\u56fe\u8868\u5df2\u751f\u6210\u3002<br>\u6df7\u6dc6\u77e9\u9635\u56fe\u8868: \u7535\u5546\u7528\u6237\u6d41\u5931\u9884\u6d4b\u62a5\u544a_\u6df7\u6dc6\u77e9\u9635_Random_Forest.png<\/li>\n<\/ul>\n\n\n\n<p>&#8212; 4. \u5173\u952e\u9a71\u52a8\u56e0\u7d20\u5206\u6790 &#8212;<br>\u901a\u8fc7\u5206\u6790\u6700\u4f73\u6a21\u578b\u7684\u7279\u5f81\u91cd\u8981\u6027\uff0c\u8bc6\u522b\u51fa\u5f71\u54cd\u7528\u6237\u6d41\u5931\u7684\u6700\u5173\u952e\u56e0\u7d20\u3002<br>\u7279\u5f81\u91cd\u8981\u6027\u6392\u5e8f (Top 10):<br>feature importance<br>days_since_last_purchase 0.244458<br>cart_abandonment_rate 0.097512<br>discount_usage_freq 0.094746<br>total_spent 0.094166<br>avg_order_value 0.092141<br>tenure_days 0.091131<br>age 0.076076<br>monthly_visits 0.056469<br>total_orders 0.054752<br>customer_service_contacts 0.044378<br>\u4ece\u4e0a\u8868\u53ef\u4ee5\u770b\u51fa\uff0c\u54ea\u4e9b\u7528\u6237\u884c\u4e3a\u548c\u5c5e\u6027\u5bf9\u6d41\u5931\u5f71\u54cd\u6700\u5927\u3002<br>\u7279\u5f81\u91cd\u8981\u6027\u56fe\u8868: \u7535\u5546\u7528\u6237\u6d41\u5931\u9884\u6d4b\u62a5\u544a_\u7279\u5f81\u91cd\u8981\u6027_Random_Forest.png<\/p>\n\n\n\n<p>&#8212; 5. \u4e1a\u52a1\u5e94\u7528\u4e0e\u5efa\u8bae &#8212;<\/p>\n\n\n\n<ol>\n<li>\u9ad8\u98ce\u9669\u7528\u6237\u8bc6\u522b: \u5229\u7528\u6700\u4f73\u6a21\u578b\u5bf9\u6240\u6709\u7528\u6237\u8ba1\u7b97\u6d41\u5931\u6982\u7387\uff0c\u7b5b\u9009\u51fa\u9ad8\u98ce\u9669\u7528\u6237\u5217\u8868\u3002<\/li>\n\n\n\n<li>\u7cbe\u51c6\u5e72\u9884:<\/li>\n<\/ol>\n\n\n\n<ul>\n<li>\u5bf9\u4e8e&#8217;\u8ddd\u79bb\u4e0a\u6b21\u8d2d\u4e70\u5929\u6570&#8217;\u957f\u7684\u7528\u6237\uff0c\u53ef\u63a8\u9001\u53ec\u56de\u4f18\u60e0\u5238\u3002<\/li>\n\n\n\n<li>\u5bf9\u4e8e&#8217;\u8d2d\u7269\u8f66\u653e\u5f03\u7387&#8217;\u9ad8\u7684\u7528\u6237\uff0c\u53ef\u5206\u6790\u652f\u4ed8\u6d41\u7a0b\u6216\u63d0\u4f9b\u5ba2\u670d\u5e2e\u52a9\u3002<\/li>\n\n\n\n<li>\u5bf9\u4e8e&#8217;\u6708\u5747\u8bbf\u95ee\u6b21\u6570&#8217;\u5c11\u7684\u7528\u6237\uff0c\u53ef\u901a\u8fc7\u90ae\u4ef6\/SMS\u63a8\u9001\u4e2a\u6027\u5316\u5185\u5bb9\u3002<\/li>\n<\/ul>\n\n\n\n<ol>\n<li>\u4ea7\u54c1\u4e0e\u8fd0\u8425\u4f18\u5316:<\/li>\n<\/ol>\n\n\n\n<ul>\n<li>\u6839\u636e\u5173\u952e\u7279\u5f81\u4f18\u5316\u7528\u6237\u5f15\u5bfc\u548c\u7559\u5b58\u7b56\u7565\u3002<\/li>\n\n\n\n<li>\u9488\u5bf9\u4e0d\u540c\u4f1a\u5458\u7b49\u7ea7\u5236\u5b9a\u5dee\u5f02\u5316\u7684\u5fe0\u8bda\u5ea6\u8ba1\u5212\u3002<\/li>\n<\/ul>\n\n\n\n<ol>\n<li>\u6a21\u578b\u8fed\u4ee3: \u5b9a\u671f\u4f7f\u7528\u6700\u65b0\u6570\u636e\u91cd\u65b0\u8bad\u7ec3\u6a21\u578b\uff0c\u4ee5\u9002\u5e94\u5e02\u573a\u548c\u7528\u6237\u884c\u4e3a\u7684\u53d8\u5316\u3002<\/li>\n<\/ol>\n\n\n\n<p>==================================================<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud83e\udde9 \u4e00\u3001\u6574\u4f53\u67b6\u6784\u6982\u89c8 \u8be5\u811a\u672c\u662f\u4e00\u4e2a\u5b8c\u6574\u7684 \u7aef\u5230\u7aef\uff08End-to-End\uff09\u7528\u6237\u6d41\u5931\u9884\u6d4b\u9879\u76ee\u539f\u578b\uff0c\u6db5\u76d6\u4e86&hellip; <a href=\"http:\/\/viplao.com\/index.php\/2025\/10\/18\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e7%94%b5%e5%95%86%e7%94%a8%e6%88%b7%e6%b5%81%e5%a4%b1%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%bb%a3%e7%a0%81%e6%b7%b1%e5%ba%a6%e8%a7%a3\/\" class=\"more-link read-more\" rel=\"bookmark\">\u7ee7\u7eed\u9605\u8bfb <span class=\"screen-reader-text\">\u3010PYTHON\u5b9e\u8df5\u6848\u4f8b\u3011\u7535\u5546\u7528\u6237\u6d41\u5931\u9884\u6d4b\u6a21\u578b\u4ee3\u7801\u6df1\u5ea6\u89e3\u6790<\/span><i class=\"fa fa-arrow-right\"><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[28],"views":712,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4065"}],"collection":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/comments?post=4065"}],"version-history":[{"count":2,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4065\/revisions"}],"predecessor-version":[{"id":4092,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4065\/revisions\/4092"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=4065"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=4065"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=4065"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}