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regression\uff09\uff0c\u652f\u6301\u5411\u91cf\u673a\uff08support vector machine\uff0cSVN\uff09\uff0c\u6216k\u8fd1\u90bb\uff08k nearest neighbors\uff0cKNN\uff09\u7b49\u7b97\u6cd5\u4f1a\u53d7\u5230\u5f88\u5927\u5f71\u54cd\u3002\u8fd9\u4e9b\u95ee\u9898\u7684\u51fa\u73b0\u5728\u4e8e\u8303\u56f4\u5927\u7684\u8bdd\u4f1a\u5bfc\u81f4\u529f\u80fd\u7684\u9ad8\u5ea6\u53d8\u5316\uff0c\u56e0\u6b64\uff0c\u5b83\u4eec\u53ef\u80fd\u53d8\u5f97\u591a\u4f59\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60a8\u5c06\u4e24\u8005\u90fd\u5f53\u4f5c\u672a\u5904\u7406\u7684\u6295\u5165\u4f7f\u7528\uff0c\u90a3\u4e48\u85aa\u6c34\u7684\u6570\u636e\u53ef\u80fd\u4f1a\u83b7\u5f97\u6bd4\u5e74\u9f84\u66f4\u91cd\u7684\u6743\u91cd\u3002<\/p>\n 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\u89e3\u51b3\u65b9\u6848\uff1a\u5efa\u6a21\u5e08\u5fc5\u987b\u4ed4\u7ec6\u68c0\u67e5\u56e2\u961f\u5982\u4f55\u83b7\u53d6\u6570\u636e\u3002\u5173\u952e\u7684\u51fa\u53d1\u70b9\u662f\u8981\u4e86\u89e3\u54ea\u4e9b\u529f\u80fd\u662f\u539f\u59cb\u683c\u5f0f\uff0c\u54ea\u4e9b\u662f\u7ecf\u8fc7\u8bbe\u8ba1\u7684\u3002\u81ea\u6b64\uff0c\u5efa\u6a21\u5e08\u5c31\u53ef\u4ee5\u5728\u8fdb\u884c\u5efa\u6a21\u4e4b\u524d\u68c0\u67e5\u884d\u751f\u529f\u80fd\u7684\u5047\u8bbe\u548c\u8ba1\u7b97\u3002<\/p>\n \u9677\u96318\uff1a\u5ffd\u7565\u591a\u7ebf\u6295\u5165\uff08multi-collinear inputs\uff09<\/strong><\/div>\n \u4f7f\u7528\u6570\u636e\u96c6\u800c\u4e0d\u8003\u8651\u591a\u91cd\u5171\u7ebf\u6027\u9884\u6d4b\u56e0\u5b50\uff08multi-collinear predictors\uff09\u662f\u8bef\u5bfc\u6a21\u578b\u5efa\u6784\u7684\u53e6\u4e00\u79cd\u65b9\u5f0f\uff08\u591a\u7ebf\u6027\u8f93\u5165\u7684\u5b58\u5728\u610f\u5473\u7740\u4e24\u4e2a\u6216\u591a\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u7740\u5f88\u9ad8\u7684\u76f8\u5173\u6027\uff09\u3002\u7ed3\u679c\u4f7f\u5176\u5f88\u96be\u8bc6\u522b\u4efb\u4f55\u4e00\u4e2a\u53d8\u91cf\u7684\u5f71\u54cd\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u9009\u5b9a\u529f\u80fd\u7684\u5fae\u5c0f\u53d8\u5316\u4f1a\u5bf9\u7ed3\u679c\u4ea7\u751f\u91cd\u5927\u5f71\u54cd\u3002\u8fd9\u4e2a\u95ee\u9898\u7684\u4e00\u4e2a\u4f8b\u5b50\u662f\uff0c\u5e7f\u544a\u9884\u7b97\u548c\u6d41\u91cf\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\u5448\u73b0\u5171\u7ebf\u6027\u3002<\/p>\n \u89e3\u51b3\u65b9\u6848\uff1a\u68c0\u6d4b\u591a\u91cd\u5171\u7ebf\u6027\u7684\u7b80\u5355\u65b9\u6cd5\u662f\u8ba1\u7b97\u6240\u6709\u53d8\u91cf\u5bf9\u5e94\u7684\u76f8\u5173\u7cfb\u6570\u3002\u4e4b\u540e\u60a8\u5c31\u6709\u8bf8\u591a\u9009\u62e9\u6765\u89e3\u51b3\u4efb\u4f55\u786e\u5b9a\u7684\u5171\u7ebf\u6027\u95ee\u9898\uff0c\u5982\u5efa\u7b51\u6784\u56fe\u6216\u5220\u9664\u5197\u4f59\u53d8\u91cf\u3002<\/p>\n \u9677\u96319\uff1a\u65e0\u6548\u7ee9\u6548KPI<\/strong><\/div>\n \u5f53\u5efa\u6a21\u6570\u636e\u5404\u79cd\u8fdb\u7a0b\u8fdb\u5165\u5e73\u8861\u72b6\u6001\u65f6\uff0c\u5927\u591a\u6570\u5efa\u6a21\u7b97\u6cd5\u8868\u73b0\u6700\u597d\u3002\u5f53\u6570\u636e\u663e\u793a\u4e0d\u5e73\u8861\u65f6\uff0c\u8861\u91cf\u6a21\u578b\u6027\u80fd\u7684\u6b63\u786e\u6307\u6807\u53d8\u7684\u81f3\u5173\u91cd\u8981\u3002\u4f8b\u5982\uff0c\u5e73\u5747\u8fdd\u7ea6\u7387\u4e3a1.2%\u3002\u4e00\u4e2a\u6a21\u578b\u7684\u51c6\u786e\u5ea6\u80fd\u8fbe\u523098%\uff0c\u9884\u6d4b\u5728\u6240\u6709\u60c5\u51b5\u4e0b\u90fd\u4e0d\u4f1a\u53d1\u751f\u53d8\u5316\u3002<\/p>\n \u89e3\u51b3\u65b9\u6848\uff1a\u9664\u975e\u53ef\u4ee5\u9009\u62e9\u751f\u6210\u66f4\u5747\u8861\u7684\u8bad\u7ec3\u96c6\uff0c\u6216\u4f7f\u7528\u57fa\u4e8e\u6210\u672c\u7684\u5b66\u4e60\u7b97\u6cd5\uff0c\u9009\u62e9\u4e1a\u52a1\u9a71\u52a8\u7684\u7ee9\u6548\u6307\u6807\u662f\u6700\u597d\u7684\u89e3\u51b3\u65b9\u6848\u3002\u5bf9\u4e8e\u8d85\u51fa\u51c6\u786e\u5ea6\u7684\u6a21\u578b\u7684\u7ee9\u6548\u6709\u7740\u5404\u79cd\u63aa\u65bd\uff0c\u5982\u7cbe\u786e\u5ea6\uff0c\u53ec\u56de\u7387\uff0cF1\u5f97\u5206\u548c\u53d7\u8bd5\u8005\u5de5\u4f5c\u7279\u5f81\uff08receiver operating characteristic\uff0cROC\uff09\u66f2\u7ebf\u3002\u9009\u62e9\u6700\u5408\u9002\u7684\u5ea6\u91cf\u6807\u51c6\u5c06\u6307\u5bfc\u5efa\u6a21\u7b97\u6cd5\u9519\u8bef\u6700\u5c0f\u5316\u3002<\/p>\n \u4ece\u575a\u5b9e\u7684\u57fa\u7840\u5f00\u59cb<\/strong><\/div>\n \u7531\u4e8e\u6280\u672f\u548c\u5de5\u5177\u7684\u8fdb\u6b65\uff0c\u673a\u5668\u5b66\u4e60\u57f9\u8bad\u9879\u76ee\u6bd4\u4ee5\u5f80\u66f4\u5bb9\u6613\u6267\u884c\u3002\u4f46\u662f\uff0c\u8981\u83b7\u5f97\u53ef\u9760\u7684\u7ed3\u679c\u9700\u8981\u5bf9\u6570\u636e\u79d1\u5b66\u548c\u7edf\u8ba1\u5b66\u539f\u7406\u6709\u6df1\u5165\u7684\u4e86\u89e3\uff0c\u5982\u6b64\u624d\u80fd\u786e\u4fdd\u56e2\u961f\u4ece\u4e00\u4e2a\u575a\u4e0d\u53ef\u6467\u7684\u5e95\u5c42\u6570\u636e\u96c6\u5f00\u59cb\uff0c\u8fd9\u8fb9\u662f\u6210\u529f\u7684\u57fa\u7840\u3002<\/p>\n Pejman Makhfi\u662fCredit Sesame\u7684\u9996\u5e2d\u6280\u672f\u5b98\u3002Credit Sesame\u662f\u4e00\u4e2a\u6559\u80b2\u4fe1\u8d37\u548c\u4e2a\u4eba\u8d22\u52a1\u7f51\u7ad9\uff0c\u4e3a\u6d88\u8d39\u8005\u63d0\u4f9b\u514d\u8d39\u7684\u4fe1\u7528\u8bc4\u5206\u670d\u52a1\u3002<\/p>\n \n |