\u6211\u662f\u5c0fz<\/p>\n
\u672c\u6b21\u5206\u4eab\u4e00\u4e2a\u6570\u636e\u6316\u6398\u5b9e\u6218\u9879\u76ee\uff1a\u4e2a\u4eba\u4fe1\u8d37\u8fdd\u7ea6\u9884\u6d4b\uff0c\u6b64\u9879\u76ee\u5bf9\u4e8e\u60f3\u8981\u5b66\u4e60\u4fe1\u8d37\u98ce\u63a7\u6a21\u578b\u7684\u540c\u5b66\u975e\u5e38\u6709\u5e2e\u52a9\uff0c\u6570\u636e\u6e90\u5728\u6587\u672b\u3002<\/p>\n
\u9879\u76ee\u80cc\u666f<\/p>\n
\u5f53\u4eca\u793e\u4f1a\uff0c\u4e2a\u4eba\u4fe1\u8d37\u4e1a\u52a1\u53d1\u5c55\u8fc5\u901f\uff0c\u4f46\u540c\u65f6\u4e5f\u4f1a\u66b4\u9732\u8f83\u9ad8\u7684\u4fe1\u7528\u98ce\u9669\u3002\u4fe1\u606f\u4e0d\u5bf9\u79f0\u5728\u91d1\u878d\u8d37\u6b3e\u9886\u57df\u7a81\u51fa\uff0c\u8868\u73b0\u5728\u8fc7\u53bb\u65f6\u671f\u501f\u6b3e\u4e00\u65b9\u5bf9\u81ea\u8eab\u7684\u8d22\u52a1\u72b6\u51b5\u3001\u8fd8\u6b3e\u80fd\u529b\u53ca\u8fd8\u6b3e\u610f\u613f\u6709\u7740\u8f83\u4e3a\u5168\u9762\u7684\u638c\u63e1\uff0c\u800c\u91d1\u878d\u673a\u6784\u4e0d\u80fd\u5168\u9762\u83b7\u77e5\u501f\u6b3e\u65b9\u7684\u98ce\u9669\u6c34\u5e73\uff0c\u6216\u5728\u76f8\u5173\u4fe1\u606f\u7684\u638c\u63e1\u4e0a\u5177\u6709\u660e\u663e\u7684\u6ede\u540e\u6027\u3002\u8fd9\u79cd\u4fe1\u606f\u52a3\u52bf\uff0c\u4f7f\u5f97\u91d1\u878d\u673a\u6784\u5728\u8d37\u6b3e\u8fc7\u7a0b\u4e2d\u53ef\u80fd\u7531\u4e8e\u98ce\u9669\u8bc4\u4f30\u4e0e\u5b9e\u9645\u60c5\u51b5\u7684\u504f\u79bb\uff0c\u4ea7\u751f\u8d44\u91d1\u635f\u5931\uff0c\u76f4\u63a5\u5f71\u54cd\u91d1\u878d\u673a\u6784\u7684\u5229\u6da6\u6c34\u5e73\u3002<\/p>\n
\u800c\u73b0\u4eca\u65f6\u95f4\u91d1\u878d\u673a\u6784\u53ef\u4ee5\u7ed3\u5408\u591a\u65b9\u6570\u636e\uff0c\u63d0\u524d\u5bf9\u5ba2\u6237\u98ce\u9669\u6c34\u5e73\u8fdb\u884c\u8bc4\u4f30\uff0c\u5e76\u505a\u51fa\u6388\u4fe1\u51b3\u7b56\u3002<\/p>\n
\u89e3\u51b3\u65b9\u6cd5<\/p>\n
\u8fd0\u7528\u5206\u7c7b\u7b97\u6cd5\u9884\u6d4b\u8fdd\u7ea6<\/p>\n
\u6a21\u578b\u9009\u62e9<\/p>\n
\u5355\u6a21\u578b\uff1a \u51b3\u7b56\u6811\u3001\u8d1d\u53f6\u65af\u3001SVM\u7b49<\/p>\n
\u96c6\u6210\u6a21\u578b\uff1a \u968f\u673a\u68ee\u6797\u3001\u68af\u5ea6\u63d0\u5347\u6811\u7b49<\/p>\n
\u8bc4\u5206\u5361\u6a21\u578b\uff1a \u903b\u8f91\u56de\u5f52<\/p>\n
\u9879\u76ee\u53ef\u8f93\u51fa\uff1a \u8bc4\u5206\u5361<\/p>\n
\u6570\u636e\u63cf\u8ff0 \u6570\u636e\u603b\u4f53\u6982\u8ff0<\/p>\n
\u53ef\u7528\u7684\u8bad\u7ec3\u6570\u636e\u5305\u62ec\u7528\u6237\u7684\u57fa\u672c\u5c5e\u6027user_info.txt\u3001\u94f6\u884c\u6d41\u6c34\u8bb0\u5f55bank_detail.txt\u3001\u7528\u6237\u6d4f\u89c8\u884c\u4e3abrowse_history.txt\u3001\u4fe1\u7528\u5361\u8d26\u5355\u8bb0\u5f55bill_detail.txt\u3001\u653e\u6b3e\u65f6\u95f4loan_time.txt\uff0c\u4ee5\u53ca\u8fd9\u4e9b\u987e\u5ba2\u662f\u5426\u53d1\u751f\u903e\u671f\u884c\u4e3a\u7684\u8bb0\u5f55overdue.txt\u3002\uff08\u6ce8\u610f\uff1a\u5e76\u975e\u6bcf\u4e00\u4f4d\u7528\u6237\u90fd\u6709\u975e\u5e38\u5b8c\u6574\u7684\u8bb0\u5f55\uff0c\u5982\u6709\u4e9b\u7528\u6237\u5e76\u6ca1\u6709\u4fe1\u7528\u5361\u8d26\u5355\u8bb0\u5f55\uff0c\u6709\u4e9b\u7528\u6237\u5374\u6ca1\u6709\u94f6\u884c\u6d41\u6c34\u8bb0\u5f55\u3002\uff09<\/p>\n
\u76f8\u5e94\u5730\uff0c\u8fd8\u6709\u7528\u4e8e\u6d4b\u8bd5\u7684\u7528\u6237\u7684\u57fa\u672c\u5c5e\u6027\u3001\u94f6\u884c\u6d41\u6c34\u3001\u4fe1\u7528\u5361\u8d26\u5355\u8bb0\u5f55\u3001\u6d4f\u89c8\u884c\u4e3a\u3001\u653e\u6b3e\u65f6\u95f4\u7b49\u6570\u636e\u4fe1\u606f\uff0c\u4ee5\u53ca\u5f85\u9884\u6d4b\u7528\u6237\u7684id\u5217\u8868\u3002<\/p>\n
\u8131\u654f\u5904\u7406\uff1a(a) \u9690\u85cf\u4e86\u7528\u6237\u7684id\u4fe1\u606f\uff1b(b) \u5c06\u7528\u6237\u5c5e\u6027\u4fe1\u606f\u5168\u90e8\u6570\u5b57\u5316\uff1b(c) \u5c06\u65f6\u95f4\u6233\u548c\u6240\u6709\u91d1\u989d\u7684\u503c\u90fd\u505a\u4e86\u51fd\u6570\u53d8\u6362\u3002<\/p>\n
\uff081\uff09\u7528\u6237\u7684\u57fa\u672c\u5c5e\u6027user_info.txt\u3002\u51716\u4e2a\u5b57\u6bb5\uff0c\u5176\u4e2d\u5b57\u6bb5\u6027\u522b\u4e3a0\u8868\u793a\u6027\u522b\u672a\u77e5\u3002<\/p>\n
\u7528\u6237id,\u6027\u522b,\u804c\u4e1a,\u6559\u80b2\u7a0b\u5ea6,\u5a5a\u59fb\u72b6\u6001,\u6237\u53e3\u7c7b\u578b 6346,1,2,4,4,2<\/p>\n
\uff082\uff09\u94f6\u884c\u6d41\u6c34\u8bb0\u5f55bank_detail.txt\u3002\u51715\u4e2a\u5b57\u6bb5\uff0c\u5176\u4e2d\uff0c\u7b2c2\u4e2a\u5b57\u6bb5\uff0c\u65f6\u95f4\u6233\u4e3a0\u8868\u793a\u65f6\u95f4\u672a\u77e5\uff1b\u7b2c3\u4e2a\u5b57\u6bb5\uff0c\u4ea4\u6613\u7c7b\u578b\u6709\u4e24\u4e2a\u503c\uff0c1\u8868\u793a\u652f\u51fa\u30010\u8868\u793a\u6536\u5165\uff1b\u7b2c5\u4e2a\u5b57\u6bb5\uff0c\u5de5\u8d44\u6536\u5165\u6807\u8bb0\u4e3a1\u65f6\uff0c\u8868\u793a\u5de5\u8d44\u6536\u5165\u3002<\/p>\n
\u7528\u6237id,\u65f6\u95f4\u6233,\u4ea4\u6613\u7c7b\u578b,\u4ea4\u6613\u91d1\u989d,\u5de5\u8d44\u6536\u5165\u6807\u8bb0 6951,5894316387,0,13.756664,0<\/p>\n
\uff083\uff09\u7528\u6237\u6d4f\u89c8\u884c\u4e3abrowse_history.txt\u3002\u51714\u4e2a\u5b57\u6bb5\u3002\u5176\u4e2d\uff0c\u7b2c2\u4e2a\u5b57\u6bb5\uff0c\u65f6\u95f4\u6233\u4e3a0\u8868\u793a\u65f6\u95f4\u672a\u77e5\u3002<\/p>\n
\u7528\u6237id,\u65f6\u95f4\u6233,\u6d4f\u89c8\u884c\u4e3a\u6570\u636e,\u6d4f\u89c8\u5b50\u884c\u4e3a\u7f16\u53f7 34724,5926003545,172,1<\/p>\n
\uff084\uff09\u4fe1\u7528\u5361\u8d26\u5355\u8bb0\u5f55bill_detail.txt\u3002\u517115\u4e2a\u5b57\u6bb5\uff0c\u5176\u4e2d\uff0c\u7b2c2\u4e2a\u5b57\u6bb5\uff0c\u65f6\u95f4\u6233\u4e3a0\u8868\u793a\u65f6\u95f4\u672a\u77e5\u3002\u4e3a\u65b9\u4fbf\u6d4f\u89c8\uff0c\u5b57\u6bb5\u4ee5\u8868\u683c\u7684\u5f62\u5f0f\u7ed9\u51fa\u3002<\/p>\n
\uff086\uff09\u987e\u5ba2\u662f\u5426\u53d1\u751f\u903e\u671f\u884c\u4e3a\u7684\u8bb0\u5f55overdue.txt\u3002\u51712\u4e2a\u5b57\u6bb5\u3002\u6837\u672c\u6807\u7b7e\u4e3a1\uff0c\u8868\u793a\u903e\u671f30\u5929\u4ee5\u4e0a\uff1b\u6837\u672c\u6807\u7b7e\u4e3a0\uff0c\u8868\u793a\u903e\u671f10\u5929\u4ee5\u5185\u3002<\/p>\n
\u6ce8\u610f\uff1a\u903e\u671f10\u5929~30\u5929\u4e4b\u5185\u7684\u7528\u6237\uff0c\u5e76\u4e0d\u5728\u6b64\u95ee\u9898\u8003\u8651\u7684\u8303\u56f4\u5185\u3002\u7528\u4e8e\u6d4b\u8bd5\u7684\u7528\u6237\uff0c\u53ea\u63d0\u4f9bid\u5217\u8868\uff0c\u6587\u4ef6\u540d\u4e3atestUsers.csv\u3002<\/p>\n
\u7528\u6237id,\u6837\u672c\u6807\u7b7e 1,1 2,0 3,1<\/p>\n
\u5404\u4e2a\u6570\u636e\u8868\u4e4b\u95f4\u7684\u5173\u7cfb<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/p>\n
\u4ece\u8868\u4e2d\u6570\u636e\u5f97\u77e5\u5e76\u975e\u6bcf\u4e00\u4f4d\u7528\u6237\u90fd\u6709\u975e\u5e38\u5b8c\u6574\u7684\u8bb0\u5f55\uff0c\u5982\u6709\u4e9b\u7528\u6237\u5e76\u6ca1\u6709\u4fe1\u7528\u5361\u8d26\u5355\u8bb0\u5f55\uff0c\u6709\u4e9b\u7528\u6237\u5374\u6ca1\u6709\u94f6\u884c\u6d41\u6c34\u8bb0\u5f55\u3002<\/p>\n
\u53d1\u73b0\u7528\u6237\u4fe1\u606f\u8868\uff0c\u662f\u5426\u903e\u671f\u8868\uff0c\u653e\u6b3e\u65f6\u95f4\u8868\u8fd9\u4e09\u5f20\u8868\u7684id\u6570\u76ee\u90fd\u662f55,596\uff0c\u94f6\u884c\u6d41\u6c34\u8868\u4e3a9,294\uff0c\u6d4f\u89c8\u4fe1\u606f\u8868\u4e3a47,330\uff0c\u4fe1\u7528\u5361\u8d26\u5355\u8868\u4e3a53,174\u3002\u901a\u8fc7\u7528\u6237id\u6570\u5f97\u5230\u5e76\u975e\u6bcf\u4e2a\u7528\u6237\u90fd\u6709\u94f6\u884c\u6d41\u6c34\u8bb0\u5f55\u3001\u4fe1\u7528\u5361\u8d26\u5355\u7b49\u4fe1\u606f\uff0c\u6240\u4ee5\u8fd9\u91cc\u6211\u4eec\u53d66\u4e2a\u8868\u5171\u540c\u7528\u6237\u7684\u8bb0\u5f55\u7b5b\u9009\u540e\u7ec4\u6210\u5b8c\u6574\u7684\u8868\u3002<\/p>\n
\u6211\u4eec\u8981\u9884\u6d4b\u7684\u6d4b\u8bd5\u96c6\u90fd\u662f\u8fd8\u6ca1\u6709\u653e\u6b3e\u7684\u7528\u6237\u7279\u5f81\uff0c\u6240\u4ee5\u8bad\u7ec3\u6570\u636e\u8fd9\u91cc\u6211\u4eec\u4e5f\u9009\u53d6\u653e\u6b3e\u65f6\u95f4\u4e4b\u524d\u7684\u7279\u5f81\uff0c\u5c06\u5b58\u5728\u65f6\u95f4\u6233\u7684\u8868\u4e0e\u653e\u6b3e\u65f6\u95f4\u8868\u8fdb\u884c\u4ea4\u53c9\uff0c\u53ea\u7b5b\u9009\u6b64\u65f6\u95f4\u8303\u56f4\u5185\u7684\u7528\u6237id\u3002<\/p>\n
\u7b5b\u9009\u51fa\u8fd96\u5f20\u8868\u5171\u6709\u7684\u7528\u6237id\uff0c\u5f97\u51fa5735\u4e2a\u7528\u6237\u7684\u8bb0\u5f55\u662f\u5b8c\u6574\u7684\u3002<\/p>\n
user.T\n<\/code><\/pre>\n<\/p>\n\u94f6\u884c\u8d26\u5355\u8868<\/p>\n
bank_detail_select\u00a0=\u00a0pd.merge(left=df_bank_detail_train,\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0right=user,\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0how='inner',\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0on='\u7528\u6237id')\n<\/code><\/pre>\n<\/p>\n\u7edf\u8ba1\u7528\u6237\u8fdb\u8d26\u5355\u6570\uff0c\u6c42\u548c<\/p>\n
\u7edf\u8ba1\u7528\u6237\u652f\u51fa\u5355\u6570\uff0c\u6c42\u548c<\/p>\n
\u7edf\u8ba1\u7528\u6237\u5de5\u8d44\u6536\u5165\u8ba1\u6570\uff0c\u6c42\u548c<\/p>\n
\u94f6\u884c\u8d26\u5355\u8868<\/p>\n
bank_train.head()\n<\/code><\/pre>\n<\/p>\n\u6d4f\u89c8\u8868<\/p>\n
\u5148\u5254\u96645735\u4ee5\u5916\u7684\u6570\u636e\uff0c\u518d\u7edf\u8ba1\u6bcf\u4e2a\u7528\u6237\u7684\u6d4f\u89c8\u8bb0\u5f55\uff08count\uff09<\/p>\n
browse_train.head()\n<\/code><\/pre>\n<\/p>\n\u8d26\u5355\u8868<\/p>\n
\u53bb\u6389\u4e86\u65f6\u95f4\u3001\u94f6\u884cid\u3001\u8fd8\u6b3e\u72b6\u6001\u8fd9\u51e0\u4e2a\u53d8\u91cf\uff0c\u6309\u7528\u6237id\u5206\u7ec4\u540e\u5bf9\u6bcf\u4e2a\u5b57\u6bb5\u5747\u503c\u5316\u5904\u7406\u3002<\/p>\n
\u903e\u671f\u8868\u3001\u7528\u6237\u8868<\/p>\n
\u5408\u5e76\u4e94\u5f20\u8868<\/p>\n
\u5c06\u7b5b\u9009\u540e\u7684\u4e94\u4e2a\u8868\u8fdb\u884c\u5408\u5e76\uff0c\u5f97\u51fa25\u4e2a\u5b57\u6bb5<\/p>\n
df_train=user_train.merge(bank_train)\ndf_train=df_train.merge(bill_train)\ndf_train=df_train.merge(browse_train)\ndf_train=df_train.merge(overdue_train)\ndf_train.head()\n<\/code><\/pre>\n<\/p>\n\u67e5\u770b\u5b8c\u6574\u8868\u683c\u7684\u57fa\u672c\u60c5\u51b5\uff0c\u65e0\u7f3a\u5931\u503c\uff0c\u5747\u662f\u6570\u503c\u7c7b\u578b\u3002<\/p>\n
df_train.info()\n<\/code><\/pre>\n<\/p>\n\u7279\u5f81\u5de5\u7a0b \u57fa\u4e8e\u4e1a\u52a1\u7406\u89e3\u7684\u7b5b\u9009 \u94f6\u884c\u6d41\u6c34\u8bb0\u5f55\u7279\u5f81\u76f8\u5173\u6027\u5206\u6790<\/p>\n
#\u00a0\u76f8\u5173\u6027\u7ed3\u679c\u6570\u636e\u8868\ncorrmat=bank_train[internal_chars].corr()\u00a0\u00a0\n#\u70ed\u529b\u56fe\nsns.heatmap(corrmat,\u00a0square=True,\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0linewidths=.5,\u00a0annot=True);\u00a0\n<\/code><\/pre>\n<\/p>\n\u603b\u8868\u76f8\u5173\u6027\u5206\u6790<\/p>\n
#\u00a0\u76f8\u5173\u6027\u7ed3\u679c\u6570\u636e\u8868\ncorrmat=df_train[internal_chars].corr()\n#\u00a0\u70ed\u529b\u56fe\nsns.heatmap(corrmat,\u00a0square=False,\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0linewidths=.5,\u00a0annot=True);\u00a0\u00a0\n<\/code><\/pre>\n<\/p>\n\u672c\u671f\u7684\u8d26\u5355\u4f59\u989d\u4e0e\u6700\u4f4e\u8fd8\u6b3e\u989d\u5177\u6709\u9ad8\u5ea6\u5171\u7ebf\u6027\uff0c\u51b3\u5b9a\u53ea\u9009\u7528\u6700\u4f4e\u8fd8\u6b3e\u989d\u3002<\/p>\n
\u751f\u4ea7\u884d\u5c04\u53d8\u91cf<\/p>\n
\u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d =\u4e0a\u671f\u8d26\u5355\u91d1\u989d - \u4e0a\u671f\u8fd8\u6b3e\u91d1\u989d\uff0c \u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d\u8fd8\u4f1a\u76f4\u63a5\u5f71\u54cd\u7528\u6237\u7684\u4fe1\u7528\u989d\u5ea6\u4ee5\u53ca\u672c\u671f\u7684\u8d26\u5355\u91d1\u989d\u3002<\/p>\n
\u8c03\u6574\u91d1\u989d\u548c\u5faa\u73af\u5229\u606f\u662f\u8ddf\u201c\u4e0a\u671f\u7684\u8fd8\u6b3e\u5dee\u989d\u201d\u6709\u5173\u7684\uff1a<\/p>\n
\u53ef\u4ee5\u5c06\u8fd8\u6b3e\u5dee\u989d\u8fdb\u884c\u201c\u7279\u5f81\u4e8c\u503c\u5316\u201d\u6765\u4ee3\u66ff\u8fd9\u4e24\u4e2a\u7279\u5f81\u3002<\/p>\n
\u9884\u501f\u73b0\u91d1\u989d\u5ea6\uff0c\u662f\u6307\u6301\u5361\u4eba\u4f7f\u7528\u4fe1\u7528\u5361\u901a\u8fc7ATM\u7b49\u81ea\u52a9\u7ec8\u7aef\u63d0\u53d6\u73b0\u91d1\u7684\u6700\u9ad8\u989d\u5ea6\uff0c\u53d6\u73b0\u989d\u5ea6\u5305\u542b\u4e8e\u4fe1\u7528\u989d\u5ea6\u4e4b\u5185\uff0c\u4e00\u822c\u662f\u4fe1\u7528\u989d\u5ea6\u768450%\u5de6\u53f3\uff0c\u6240\u4ee5\u53ef\u4ee5\u4e0d\u7528\u8fd9\u4e2a\u7279\u5f81\uff0c\u9009\u62e9\u4fe1\u7528\u989d\u5ea6\u5373\u53ef\u3002<\/p>\n
df_train['\u5e73\u5747\u652f\u51fa']=df_train.apply(lambda\u00a0x:x.\u652f\u51fa\u91d1\u989d\/x.\u652f\u51fa\u5355\u6570,\u00a0axis=1)\u00a0\u00a0\ndf_train['\u5e73\u5747\u5de5\u8d44\u6536\u5165']=df_train.apply(lambda\u00a0x:x.\u5de5\u8d44\u6536\u5165\/x.\u5de5\u8d44\u7b14\u6570,\u00a0axis=1)\ndf_train['\u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d']=df_train.apply(lambda\u00a0x:x.\u4e0a\u671f\u8d26\u5355\u91d1\u989d-x.\u4e0a\u671f\u8fd8\u6b3e\u91d1\u989d,\u00a0axis=1)\ndf_select=df_train.loc[:,['\u7528\u6237id',\u00a0'\u6027\u522b',\u00a0'\u6559\u80b2\u7a0b\u5ea6',\u00a0'\u5a5a\u59fb\u72b6\u6001',\u00a0'\u5e73\u5747\u652f\u51fa',\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'\u5e73\u5747\u5de5\u8d44\u6536\u5165',\u00a0'\u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d',\u00a0'\u4fe1\u7528\u5361\u989d\u5ea6',\u00a0'\u672c\u671f\u8d26\u5355\u4f59\u989d',\u00a0'\u672c\u671f\u8d26\u5355\u6700\u4f4e\u8fd8\u6b3e\u989d',\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'\u6d88\u8d39\u7b14\u6570',\u00a0\u00a0'\u6d4f\u89c8\u884c\u4e3a\u6570\u636e',\u00a0'\u6837\u672c\u6807\u7b7e']].fillna(0)\ndf_select.head()\n<\/code><\/pre>\n<\/p>\n\u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7b5b\u9009<\/p>\n
\u5c06\u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d\u4e8c\u503c\u5316<\/p>\n
from\u00a0sklearn.preprocessing\u00a0import\u00a0Binarizer\nX=df_select['\u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d'].values.reshape(-1,1)\ntransformer\u00a0=\u00a0Binarizer(threshold=0).fit_transform(X)\ndf_select['\u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d\u6807\u7b7e']=transformer\n<\/code><\/pre>\n<\/p>\n\u65b9\u5dee\u8fc7\u6ee4\u6cd5<\/p>\n
\u8fc7\u6ee4\u90a3\u4e9b\u4e0d\u5e26\u6709\u4fe1\u606f\u7684\u53d8\u91cf\uff0c\u9ed8\u8ba4\u53c2\u6570\u4e3a0\uff0c\u5373\u8fc7\u6ee4\u65b9\u5dee\u4e3a0\u7684\u90a3\u4e9b\u53d8\u91cf\uff0c\u53ea\u4fdd\u7559\u5bf9\u6a21\u578b\u6709\u8d21\u732e\u7684\u90a3\u4e9b\u4fe1\u606f\u3002<\/p>\n
from\u00a0sklearn.feature_selection\u00a0import\u00a0VarianceThreshold\nVTS\u00a0=\u00a0VarianceThreshold()\u00a0\u00a0\u00a0#\u00a0\u5b9e\u4f8b\u5316\uff0c\u53c2\u6570\u9ed8\u8ba4\u65b9\u5dee\u4e3a0\nx_01=VTS.fit_transform(x)\n<\/code><\/pre>\n<\/p>\n\u76f8\u5173\u6027\u8fc7\u6ee4--\u4e92\u4fe1\u606f\u6cd5<\/p>\n
\u4e92\u4fe1\u606f\u6cd5\u662f\u7528\u6765\u6355\u6349\u6bcf\u4e2a\u7279\u5f81\u4e0e\u6807\u7b7e\u4e4b\u95f4\u7684\u4efb\u610f\u5173\u7cfb(\u5305\u62ec\u7ebf\u6027\u548c\u975e\u7ebf\u6027\u5173\u7cfb)\u7684\u8fc7\u6ee4\u65b9\u6cd5\u3002<\/p>\n
\u548cF\u68c0\u9a8c\u76f8\u4f3c\uff0c\u5b83\u65e2\u53ef\u4ee5\u505a\u56de\u5f52\u4e5f\u53ef\u4ee5\u505a\u5206\u7c7b\uff0c\u5e76\u4e14\u5305\u542b\u4e24\u4e2a\u7c7bmutual_info_classif(\u4e92\u4fe1\u606f\u5206\u7c7b)\u548cmutual_info_regression(\u4e92\u4fe1\u606f\u56de\u5f52)\u3002<\/p>\n
\u8fd9\u4e24\u4e2a\u7c7b\u7684\u7528\u6cd5\u548c\u53c2\u6570\u90fd\u548cF\u68c0\u9a8c\u4e00\u6a21\u4e00\u6837\uff0c\u4e0d\u8fc7\u4e92\u4fe1\u606f\u6cd5\u6bd4F\u68c0\u9a8c\u66f4\u52a0\u5f3a\u5927\uff0cF\u68c0\u9a8c\u53ea\u80fd\u591f\u627e\u51fa\u7ebf\u6027\u5173\u7cfb\uff0c\u800c\u4e92\u4fe1\u606f\u6cd5\u53ef\u4ee5\u627e\u51fa\u4efb\u610f\u5173\u7cfb\u3002<\/p>\n
from\u00a0sklearn.feature_selection\u00a0import\u00a0mutual_info_classif\u00a0as\u00a0MIC\nresult\u00a0=\u00a0MIC(x,y)\n<\/code><\/pre>\n<\/p>\n\u6837\u672c\u4e0d\u5747\u8861<\/p>\n
\u901a\u8fc7\u89c2\u5bdf\uff0c\u6b63\u8d1f\u6837\u672c\u6bd4\u4f8b\u4e3a 836\uff1a4899\uff0c\u5c5e\u4e8e\u6837\u672c\u4e0d\u5747\u8861\u8303\u7574\uff0c\u53ef\u91c7\u7528\u4e0a\u91c7\u6837\u7684SMOTE\u7b97\u6cd5\u5bf9\u5176\u8fdb\u884c\u6837\u672c\u4e0d\u5747\u8861\u5904\u7406\u3002<\/p>\n
from\u00a0imblearn.over_sampling\u00a0import\u00a0SMOTE\nover_samples\u00a0=\u00a0SMOTE(random_state=111)\nover_samples_x,\u00a0over_samples_y\u00a0=\u00a0over_samples.fit_sample(x,y)\n<\/code><\/pre>\n<\/p>\n\u6a21\u578b\u5efa\u7acb\u4e0e\u8c03\u53c2<\/p>\n
\u6587\u7ae0\u4e00\u5f00\u59cb\u5df2\u7ecf\u63d0\u5230\u8fc7\u4e86\uff0c\u53ef\u9009\u6a21\u578b\u8f83\u591a\uff0c\u8fd9\u91cc\u4e3e\u4f8b\u4e09\u79cd\u6a21\u578b\u903b\u8f91\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u6a21\u578b\uff0c\u5176\u4f59\u6a21\u578b\u7684\u9009\u7528\uff0c\u5c0f\u4f19\u4f34\u4eec\u53ef\u4ee5\u81ea\u5df1\u52a8\u624b\u7ec3\u4e60\u7ec3\u4e60\u3002<\/p>\n
\u4e8c\u5206\u7c7b\u6a21\u578b\u2014\u2014\u903b\u8f91\u56de\u5f52\u6a21\u578b \u4e92\u4fe1\u606f\u4e0e\u6b63\u5219\u5316\u5bf9\u6a21\u578b\u6548\u679c\u7684\u5f71\u54cd<\/p>\n
\u7528\u5b66\u4e60\u66f2\u7ebf\u5bf9\u53c2\u6570C\u8fdb\u884c\u8c03\u6574\uff0c\u5206\u522b\u5728\u4e24\u4e2a\u6a21\u578b\u4e2d\u8fdb\u884c\u8c03\u53c2\u3002<\/p>\n
\u8d85\u53c2\u6570C : \u4e00\u822c\u4e0d\u4f1a\u8d85\u8fc71\uff0c \u8d8a\u5927\u60e9\u7f5a\u529b\u5ea6\u8d8a\u5c0f\uff0c\u672c\u6b21\u9009\u53d6\u4ece 0.05 - 2\u8303\u56f4\u3002<\/p>\n
from\u00a0sklearn.linear_model\u00a0import\u00a0LogisticRegression\u00a0as\u00a0LR\nfrom\u00a0sklearn.model_selection\u00a0import\u00a0cross_val_score\u00a0as\u00a0cvs\nlrl1\u00a0=\u00a0LR(penalty='l1',\u00a0solver='liblinear',\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0C=i,\u00a0max_iter=1000,\u00a0random_state=0)\nlrl2\u00a0=\u00a0LR(penalty='l2',\u00a0solver='liblinear',\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0C=i,\u00a0max_iter=1000,\u00a0random_state=0)\n<\/code><\/pre>\n<\/p>\n\u7531\u56fe\u53ef\u77e5\uff0c\u5728\u7ecf\u8fc7\u4e92\u4fe1\u606f\u8fc7\u6ee4\u540e\uff0c\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5f97\u5206\u660e\u663e\u63d0\u9ad8\uff0c\u4e14\u5f53\u8d85\u53c2\u6570C=0.6\u65f6\uff0c\u6a21\u578b\u6548\u679c\u662f\u6700\u597d\u7684\u3002<\/p>\n
\u5305\u88c5\u6cd5\u7b5b\u9009\u53d8\u91cf<\/p>\n
\u4ee5\u903b\u8f91\u56de\u5f52\u4e3a\u57fa\u5206\u7c7b\u5668\uff0c\u7ed3\u5408\u5305\u88c5\u6cd5\u7b5b\u9009\u53d8\u91cf\uff0c\u5e76\u8fd0\u7528\u4ea4\u53c9\u9a8c\u8bc1\u7ed8\u5236\u5b66\u4e60\u66f2\u7ebf\uff0c\u63a2\u7d22\u6700\u4f73\u53d8\u91cf\u4e2a\u6570\u3002<\/p>\n
\u540c\u65f6\uff0c\u8fd0\u7528SMOTE\u7b97\u6cd5\u8fdb\u884c\u6837\u672c\u5747\u8861\u5904\u7406\uff0c\u5e76\u6bd4\u8f83\u5747\u8861\u524d\u540e\u6a21\u578b\u6548\u679c\u7684\u53d8\u5316\u3002<\/p>\n
from\u00a0sklearn.feature_selection\u00a0import\u00a0RFE\nLR_1\u00a0=\u00a0LogisticRegression(penalty='l1',\u00a0solver='liblinear',\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0C=0.6,\u00a0max_iter=1000,\u00a0random_state=0)\nselector1\u00a0=\u00a0RFE(LR_1,\u00a0n_features_to_select=i,\u00a0step=1)\nX_wrapper1\u00a0=\u00a0selector1.fit_transform(x,\u00a0y)\nonce1=cvs(LR_1,\u00a0X_wrapper1,\u00a0y,\u00a0cv=5,\u00a0scoring='f1').mean()\n<\/code><\/pre>\n<\/p>\n\u7531\u56fe\u53ef\u89c1\uff0c\u6837\u672c\u5747\u8861\u524d\u540e\u6a21\u578b\u6548\u679c\u6709\u5927\u5e45\u5ea6\u589e\u957f\u3002\u4e14\u4e24\u79cd\u6b63\u5219\u5316\u65b9\u6cd5\u76f8\u5dee\u65e0\u51e0\u3002<\/p>\n
\u6811\u6a21\u578b\u2014\u2014\u51b3\u7b56\u6811<\/p>\n
\u56e0\u4e3a\u6837\u672c\u5747\u8861\u5316\u5904\u7406\u524d\u540e\uff0c\u5bf9\u6a21\u578b\u6548\u679c\u63d0\u5347\u8f83\u4e3a\u660e\u663e\uff0c\u56e0\u6b64\u5728\u4f7f\u7528\u51b3\u7b56\u6811\u6a21\u578b\u5efa\u7acb\u4e4b\u524d\uff0c\u5bf9\u6837\u672c\u8fdb\u884c\u5747\u8861\u5316\u5904\u7406\u3002<\/p>\n
\u56e0\u4e3a\u6df1\u5ea6\u53c2\u6570max_depth\u662f\u5bf9\u51b3\u7b56\u6811\u6a21\u578b\u5f71\u54cd\u6700\u5927\u7684\u53c2\u6570\u4e4b\u4e00\uff0c\u56e0\u6b64\u672c\u6848\u4f8b\u6b63\u5bf9\u51b3\u7b56\u6811\u6df1\u5ea6\u7ed8\u5236\u5b66\u4e60\u66f2\u7ebf\uff0c\u63a2\u7d22\u51b3\u7b56\u6811\u6700\u4f73\u53c2\u6570\u3002<\/p>\n
plt.plot(L_CVS,\u00a0'r')\u00a0\u00a0#\u00a0\u4ea4\u53c9\u9a8c\u8bc1\nplt.plot(L_train,\u00a0'g')#\u00a0\u8bad\u7ec3\u96c6\nplt.plot(L_test,\u00a0'b')\u00a0#\u00a0\u6d4b\u8bd5\u96c6\n<\/code><\/pre>\n<\/p>\n\u7531\u5b66\u4e60\u66f2\u7ebf\u53ef\u77e5\uff0c\u5728max_depth=5\u65f6\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u6a21\u578b\u6548\u679c\u5747\u8fbe\u5230\u4e86\u6700\u4f73\u72b6\u6001\uff0c\u5f53\u5728max_depth\u5927\u4e8e5\u540e\uff0c\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u5206\u6570\u4f9d\u7136\u5728\u4e0a\u5347\uff0c\u800c\u6d4b\u8bd5\u96c6\u4e0a\u7684\u8868\u73b0\u6709\u6240\u4e0b\u964d\uff0c\u8fd9\u5c31\u662f\u6a21\u578b\u8fc7\u62df\u5408\u73b0\u8c61\uff0c\u56e0\u6b64\u6700\u7ec8\u6211\u4eec\u9009\u7528max_depth=5\u3002<\/p>\n
\u7279\u5f81\u91cd\u8981\u6027<\/p>\n
features_imp\u00a0=\u00a0pd.Series(dtc.feature_importances_,\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0index\u00a0=\u00a0x.columns).sort_values(ascending=False)\nfeatures_imp\n<\/code><\/pre>\n<\/p>\n\u4e0a\u671f\u8fd8\u6b3e\u5dee\u989d\u6807\u7b7e 0.705916\n\u6027\u522b 0.101779\n\u5e73\u5747\u652f\u51fa 0.064218\n\u5e73\u5747\u5de5\u8d44\u6536\u5165 0.047644\n\u6d4f\u89c8\u884c\u4e3a\u6570\u636e 0.044333\n\u6559\u80b2\u7a0b\u5ea6 0.015257\n\u5a5a\u59fb\u72b6\u6001 0.012665\n\u672c\u671f\u8d26\u5355\u6700\u4f4e\u8fd8\u6b3e\u989d 0.004455\n\u6d88\u8d39\u7b14\u6570 0.003734\n\u672c\u671f\u8d26\u5355\u4f59\u989d 0.000000\n\u4fe1\u7528\u5361\u989d\u5ea6 0.000000\ndtype: float64\n<\/code><\/pre>\n<\/p>\n\u51b3\u7b56\u6811\u53ef\u89c6\u5316<\/p>\n
\u8fd9\u91cc\u63d0\u51fa\u4e00\u70b9\uff0c\u5982\u679c\u9700\u8981\u6df1\u5165\u7406\u89e3\u51b3\u7b56\u6811\u51b3\u7b56\u8fc7\u7a0b\uff0c\u53ef\u4ee5\u501f\u52a9\u51b3\u7b56\u6811\u53ef\u89c6\u5316\u6765\u8f85\u52a9\u7406\u89e3\u3002<\/p>\n
import\u00a0graphviz\nfrom\u00a0sklearn\u00a0import\u00a0tree\n#\u9996\u5148\u914d\u7f6e\ndot_data\u00a0=\u00a0tree.export_graphviz(dtc\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u8981\u5bf9\u5df2\u7ecf\u5efa\u6210\u7684dct\u8fd9\u4e2a\u5b9e\u4f8b\u5316\u597d\u7684\u6a21\u578b\u8fdb\u884c\u753b\u56fe\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0,feature_names=\u00a0x.columns\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u66f4\u6539\u5217\u540d\u4e3a\u4e2d\u6587\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0,class_names=[]\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u66f4\u6539\u6807\u7b7e\u540d\u5b57\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0,filled=True\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u7ed9\u6bcf\u4e00\u4e2a\u8282\u70b9\u5206\u914d\u989c\u8272,\u989c\u8272\u7ea6\u6df1\u8868\u793a\u53f6\u5b50\u7684\u7eaf\u5ea6\u8d8a\u9ad8\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0,rounded=True\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0#\u00a0\u8282\u70b9\u6027\u72b6\u4e3a\u5706\u89d2\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0)\ngraph\u00a0=\u00a0graphviz.Source(dot_data)\ngraph\n<\/code><\/pre>\n<\/p>\n\u6811\u6a21\u578b\u2014\u2014\u968f\u673a\u68ee\u6797<\/p>\n
from\u00a0sklearn.ensemble\u00a0import\u00a0RandomForestClassifier\u00a0as\u00a0RFC\nfrom\u00a0sklearn.model_selection\u00a0import\u00a0GridSearchCV\nrfc\u00a0=\u00a0RFC(n_estimators=i+1,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0n_jobs=-1,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0random_state=90)\nscore\u00a0=\u00a0cvs(rfc,over_samples_x_train,\u00a0\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0over_samples_y_train,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cv=5,\u00a0scoring='f1').mean()\n<\/code><\/pre>\n<\/p>\n\u6a21\u578b\u8c03\u53c2<\/p>\n
\u6709\u2f00\u4e9b\u53c2\u6570\u662f\u6ca1\u6709\u53c2\u7167\u7684\uff0c\u4e00\u5f00\u59cb\u5f88\u96be\u786e\u5b9a\u2f00\u4e2a\u8303\u56f4\uff0c\u8fd9\u79cd\u60c5\u51b5\u4e0b\u91c7\u7528\u5148\u901a\u8fc7\u5b66\u4e60\u66f2\u7ebf\u786e\u5b9a\u53c2\u6570\u5927\u81f4\u8303\u56f4\uff0c\u518d\u901a\u8fc7\u7f51\u683c\u641c\u7d22\u786e\u5b9a\u6700\u4f73\u53c2\u6570\u3002<\/p>\n
\u6bd4\u5982\u786e\u5b9an_estimators\u8303\u56f4\u65f6\uff0c\u901a\u8fc7\u5b66\u4e60\u66f2\u7ebf\u89c2\u5bdfn_estimators\u5728\u4ec0\u4e48\u53d6\u503c\u5f00\u59cb\u53d8\u5f97\u5e73\u7a33\uff0c\u662f\u5426\u2f00\u76f4\u63a8\u52a8\u6a21\u578b\u6574\u4f53\u51c6\u786e\u7387\u7684\u4e0a\u5347\u7b49\u4fe1\u606f\u3002<\/p>\n
\u5bf9\u4e8e\u5176\u4ed6\u53c2\u6570\u4e5f\u662f\u6309\u7167\u540c\u6837\u7684\u601d\u8def\uff0c\u5982\u5f71\u54cd\u5355\u68f5\u51b3\u7b56\u6811\u6a21\u578b\u7684\u53c2\u6570max_depth\u6765\u8bf4\uff0c\u2f00\u822c\u6839\u636e\u6570\u636e\u7684\u2f24\u2f29\u6765\u8fdb\u2f8f\u2f00\u4e2a\u8bd5\u63a2\uff0c\u6bd4\u5982\u4e73\u817a\u764c\u6570\u636e\u5f88\u2f29\uff0c\u6240\u4ee5\u53ef\u4ee5\u91c7\u2f641\uff5e10\uff0c\u6216\u80051~20\u8fd9\u6837\u7684\u8bd5\u63a2\u3002<\/p>\n
\u4f46\u5bf9\u4e8e\u50cfdigit recognition\u90a3\u6837\u7684\u2f24\u578b\u6570\u636e\u6765\u8bf4\uff0c\u6211\u4eec\u5e94\u8be5\u5c1d\u8bd530~50\u5c42\u6df1\u5ea6\uff08\u6216\u8bb8\u8fd8\u4e0d\u2f9c\u591f\uff09\uff0c\u6b64\u65f6\u66f4\u5e94\u8be5\u753b\u51fa\u5b66\u4e60\u66f2\u7ebf\uff0c\u6765\u89c2\u5bdf\u6df1\u5ea6\u5bf9\u6a21\u578b\u7684\u5f71\u54cd\u3002<\/p>\n
\u786e\u5b9a\u8303\u56f4\u540e\uff0c\u5c31\u53ef\u4ee5\u901a\u8fc7\u7f51\u683c\u641c\u7d22\u7684\u65b9\u5f0f\u786e\u5b9a\u6700\u4f73\u53c2\u6570\u3002\u5176\u4ed6\u53c2\u6570\u5c31\u4e0d\u4e00\u4e00\u4e3e\u4f8b\u4e86\uff0c\u5927\u5bb6\u53ef\u4ee5\u52a8\u624b\u5c1d\u8bd5\u4e00\u4e0b\u3002<\/p>\n
#\u00a0\u8c03\u6574max_depth\nparam_grid\u00a0=\u00a0{'max_depth':np.arange(1,\u00a020,\u00a01)}\nrfc\u00a0=\u00a0RFC(n_estimators=150,random_state=90,\u00a0n_jobs=-1)\nGS\u00a0=\u00a0GridSearchCV(rfc,param_grid,cv=5,\u00a0scoring='f1')\nGS.fit(over_samples_x,\u00a0over_samples_y)\nGS.best_params_\nGS.best_score_\n<\/code><\/pre>\n<\/p>\n\u6a21\u578b\u8bc4\u4ef7<\/p>\n
\u672c\u6b21\u6848\u4f8b\u6a21\u578b\u8bc4\u4f30\u4f7f\u7528classification_report<\/p>\n
sklearn\u4e2d\u7684classification_report\u51fd\u6570\u7528\u4e8e\u663e\u793a\u4e3b\u8981\u5206\u7c7b\u6307\u6807\u7684\u6587\u672c\u62a5\u544a\uff0e\u5728\u62a5\u544a\u4e2d\u663e\u793a\u6bcf\u4e2a\u7c7b\u7684\u7cbe\u786e\u5ea6\uff0c\u53ec\u56de\u7387\uff0cF1\u503c\u7b49\u4fe1\u606f\u3002<\/p>\n
\u4e3b\u8981\u53c2\u6570:<\/p>\n
y_true\uff1a1\u7ef4\u6570\u7ec4\uff0c\u6216\u6807\u7b7e\u6307\u793a\u5668\u6570\u7ec4\/\u7a00\u758f\u77e9\u9635\uff0c\u76ee\u6807\u503c\u3002<\/p>\n
y_pred\uff1a1\u7ef4\u6570\u7ec4\uff0c\u6216\u6807\u7b7e\u6307\u793a\u5668\u6570\u7ec4\/\u7a00\u758f\u77e9\u9635\uff0c\u5206\u7c7b\u5668\u8fd4\u56de\u7684\u4f30\u8ba1\u503c\u3002<\/p>\n
labels\uff1aarray\uff0cshape = [n_labels]\uff0c\u62a5\u8868\u4e2d\u5305\u542b\u7684\u6807\u7b7e\u7d22\u5f15\u7684\u53ef\u9009\u5217\u8868\u3002<\/p>\n
target_names\uff1a\u5b57\u7b26\u4e32\u5217\u8868\uff0c\u4e0e\u6807\u7b7e\u5339\u914d\u7684\u53ef\u9009\u663e\u793a\u540d\u79f0\uff08\u76f8\u540c\u987a\u5e8f\uff09\u3002<\/p>\n
sample_weight\uff1a\u7c7b\u4f3c\u4e8eshape = [n_samples]\u7684\u6570\u7ec4\uff0c\u53ef\u9009\u9879\uff0c\u6837\u672c\u6743\u91cd\u3002<\/p>\n
digits\uff1aint\uff0c\u8f93\u51fa\u6d6e\u70b9\u503c\u7684\u4f4d\u6570\u3002<\/p>\n
\u51b3\u7b56\u6811\u9a8c\u8bc1\u96c6\u8bc4\u4ef7\u7ed3\u679c<\/p>\n
\u6700\u540e\u8fd9\u91cc\u4e3e\u4e86\u4e00\u4e2a\u51b3\u7b56\u6811\u6a21\u578b\u6548\u679c\u8bc4\u4ef7\u7684\u4f8b\u5b50\uff0c\u5176\u4f59\u5206\u7c7b\u578b\u6a21\u578b\u8bc4\u4ef7\u540c\u6837\u53ef\u4ee5\u4f7f\u7528\u3002\u5f53\u7136\uff0c\u6a21\u578b\u8bc4\u4ef7\u65b9\u6cd5\u4e0d\u6b62\u8fd9\u4e00\u79cd\uff0c\u5927\u5bb6\u4e5f\u53ef\u4ee5\u5c1d\u8bd5\u7740\u4ece\u5176\u4ed6\u89d2\u5ea6\u6765\u505a\u6a21\u578b\u8bc4\u4ef7\u3002<\/p>\n
precision recall f1-score support\n 0 0.70 0.74 0.72 1454\n 1 0.72 0.68 0.70 1454\n accuracy 0.71 2908\n macro avg 0.71 0.71 0.71 2908\nweighted avg 0.71 0.71 0.71 2908\n<\/code><\/pre>\n<\/p>\n\u672c\u6587\u65e8\u5728\u68b3\u7406\u6570\u636e\u6316\u6398\u7684\u4e00\u822c\u8fc7\u7a0b\uff0c\u6ca1\u6709\u6d89\u53ca\u5230\u5f88\u590d\u6742\u7684\u7b97\u6cd5\uff0c\u6bcf\u4e2a\u73af\u8282\uff0c\u5982\u6570\u636e\u9884\u5904\u7406\u3001\u7279\u5f81\u5de5\u7a0b\u3001\u6a21\u578b\u5efa\u7acb\u4e8e\u8bc4\u4ef7\uff0c\u5747\u662f\u5e38\u7528\u7684\u65b9\u6cd5\u3002\u672c\u6587\u6570\u636e\u4e5f\u90fd\u7ed9\u5927\u5bb6\u51c6\u5907\u597d\u4e86\u3002<\/p>\n
\u6570\u636e\u6e90\u4ee3\u7801\u4e0b\u8f7d\u5730\u5740\uff1a<\/p>\n
\u63d0\u53d6\u7801\uff1aniub<\/p>\n","protected":false},"excerpt":{"rendered":"\u6211\u662f\u5c0fz\u672c\u6b21\u5206\u4eab\u4e00\u4e2a\u6570\u636e\u6316\u6398\u5b9e\u6218\u9879\u76ee\uff1a\u4e2a\u4eba\u4fe1\u8d37\u8fdd\u7ea6\u9884\u6d4b\uff0c\u6b64\u9879\u76ee\u5bf9\u4e8e\u60f3\u8981\u5b66\u4e60\u4fe1\u8d37\u98ce\u63a7\u6a21\u578b\u7684\u540c\u5b66\u975e\u5e38\u6709\u5e2e\u52a9\uff0c\u6570\u636e\u6e90\u5728\u6587\u672b\u3002\u9879\u76ee\u80cc\u666f\u5f53\u4eca\u793e\u4f1a\uff0c\u4e2a\u4eba\u4fe1\u8d37\u4e1a\u52a1\u53d1\u5c55\u8fc5\u901f","protected":false},"author":1,"featured_media":34361,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[8],"tags":[2888],"_links":{"self":[{"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/posts\/23151"}],"collection":[{"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/comments?post=23151"}],"version-history":[{"count":0,"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/posts\/23151\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/media\/34361"}],"wp:attachment":[{"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/media?parent=23151"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/categories?post=23151"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.962900.com\/wp-json\/wp\/v2\/tags?post=23151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}