{"id":68045,"date":"2026-01-29T16:18:00","date_gmt":"2026-01-29T08:18:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/68045.html"},"modified":"2026-01-29T16:18:00","modified_gmt":"2026-01-29T08:18:00","slug":"%e3%80%90%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92%e3%80%91%e4%b8%8e%e3%80%90%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92%e3%80%91%e5%ae%9e%e6%88%98%e8%a7%a3%e6%9e%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/68045.html","title":{"rendered":"\u3010\u7ebf\u6027\u56de\u5f52\u3011\u4e0e\u3010\u903b\u8f91\u56de\u5f52\u3011\u5b9e\u6218\u89e3\u6790"},"content":{"rendered":"<h2>\u4e00\u3001\u7ebf\u6027\u56de\u5f52&#xff1a;\u9884\u6d4b\u8fde\u7eed\u503c\u7684&#034;\u5165\u95e8\u795e\u5668&#034;<\/h2>\n<h4>1. \u6838\u5fc3\u539f\u7406&#xff1a;\u7528\u7ebf\u6027\u5173\u7cfb\u62df\u5408\u771f\u5b9e\u6570\u636e<\/h4>\n<p>\u7ebf\u6027\u56de\u5f52\u7684\u672c\u8d28\u7684\u662f\u5bfb\u627e\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u6620\u5c04\u5173\u7cfb&#xff0c;\u4ece\u800c\u9884\u6d4b\u4e00\u4e2a\u8fde\u7eed\u7684\u8f93\u51fa\u503c\u3002<\/p>\n<p>\u6570\u5b66\u516c\u5f0f&#xff1a;<img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"29\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081758-697b17b6b0d0c.png\" width=\"323\" \/><\/p>\n<ul>\n<li>\n<p>y&#xff1a;\u5f85\u9884\u6d4b\u7684\u8fde\u7eed\u503c<\/p>\n<\/li>\n<li>\n<p>X1,X2,&#8230;Xn&#xff1a;\u8f93\u5165\u7279\u5f81<\/p>\n<\/li>\n<li>\n<p>\u03b80&#xff1a;\u622a\u8ddd\u9879<\/p>\n<\/li>\n<li>\n<p>\u03b81,\u03b82,&#8230;,\u03b8n&#xff1a;\u7279\u5f81\u7cfb\u6570<\/p>\n<\/li>\n<\/ul>\n<p>\u7b97\u6cd5\u7684\u76ee\u6807&#xff0c;\u5c31\u662f\u901a\u8fc7\u6570\u636e\u8bad\u7ec3\u51fa\u6700\u4f18\u7684 \u03b8 \u503c&#xff0c;\u8ba9\u9884\u6d4b\u503c\u548c\u771f\u5b9e\u503c\u7684\u8bef\u5dee\u6700\u5c0f\u3002<\/p>\n<h4>2. \u5b9e\u6218\u6848\u4f8b&#xff1a;\u7528\u4f53\u91cd\u548c\u5e74\u9f84\u9884\u6d4b\u6536\u7f29\u538b<\/h4>\n<h5>&#xff08;1&#xff09;\u4ee3\u7801\u5b9e\u73b0<\/h5>\n<p>import pandas as pd<br \/>\nfrom sklearn.linear_model import LinearRegression<\/p>\n<p># 1. \u6570\u636e\u8bfb\u53d6<br \/>\ndata &#061; pd.read_csv(&#039;\u591a\u5143\u7ebf\u6027\u56de\u5f52.csv&#039;, encoding&#061;&#039;gbk&#039;)<\/p>\n<p># 2. \u5b9a\u4e49\u7279\u5f81&#xff08;X&#xff09;\u548c\u6807\u7b7e&#xff08;y&#xff09;<br \/>\nX &#061; data[[&#039;\u4f53\u91cd&#039;, &#039;\u5e74\u9f84&#039;]]  # \u4e24\u4e2a\u8f93\u5165\u7279\u5f81<br \/>\ny &#061; data[&#039;\u8840\u538b\u6536\u7f29&#039;]        # \u8fde\u7eed\u8f93\u51fa\u6807\u7b7e<\/p>\n<p># 3. \u6a21\u578b\u8bad\u7ec3<br \/>\nmodel &#061; LinearRegression().fit(X, y)<\/p>\n<p># 4. \u63d0\u53d6\u6a21\u578b\u53c2\u6570<br \/>\nweight_coef &#061; model.coef_[0]  # \u4f53\u91cd\u7684\u7cfb\u6570<br \/>\nage_coef &#061; model.coef_[1]     # \u5e74\u9f84\u7684\u7cfb\u6570<br \/>\nintercept &#061; model.intercept_  # \u622a\u8ddd\u9879<\/p>\n<p># 5. \u8f93\u51fa\u7ed3\u679c<br \/>\nprint(f&#034;\u4f53\u91cd\u7cfb\u6570: {weight_coef:.4f}&#034;)<br \/>\nprint(f&#034;\u5e74\u9f84\u7cfb\u6570: {age_coef:.4f}&#034;)<br \/>\nprint(f&#034;\u622a\u8ddd\u9879: {intercept:.4f}&#034;)<br \/>\nprint(f&#034;\u8840\u538b\u6536\u7f29 &#061; {intercept:.4f} &#043; {weight_coef:.4f}\u00d7\u4f53\u91cd &#043; {age_coef:.4f}\u00d7\u5e74\u9f84&#034;) <\/p>\n<h5>&#xff08;2&#xff09;\u8fd0\u884c\u7ed3\u679c<\/h5>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"198\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081758-697b17b6c4303.png\" width=\"600\" \/><\/p>\n<h4>3. \u9002\u7528\u573a\u666f<\/h4>\n<p>\u7ebf\u6027\u56de\u5f52\u9002\u5408\u8fde\u7eed\u503c\u9884\u6d4b&#xff0c;\u6bd4\u5982&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u9884\u6d4b\u623f\u4ef7\u3001\u80a1\u7968\u4ef7\u683c&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u9884\u6d4b\u5b66\u751f\u6210\u7ee9\u3001\u4ea7\u54c1\u9500\u91cf&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u9884\u6d4b\u751f\u7406\u6307\u6807&#xff08;\u5982\u8840\u538b\u3001\u8840\u7cd6&#xff09;\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>\u4e8c\u3001\u903b\u8f91\u56de\u5f52&#xff1a;\u4ece\u7ebf\u6027\u5230\u5206\u7c7b\u7684&#034;\u8fdb\u9636\u9b54\u6cd5&#034;<\/h3>\n<p>\u903b\u8f91\u56de\u5f52\u540d\u5b57\u91cc\u5e26&#034;\u56de\u5f52&#034;&#xff0c;\u4f46\u5b9e\u9645\u662f\u4e8c\u5206\u7c7b\u7b97\u6cd5\u2014\u2014\u5b83\u57fa\u4e8e\u7ebf\u6027\u56de\u5f52\u7684\u7ed3\u679c&#xff0c;\u901a\u8fc7\u4e00\u4e2a\u300csigmoid\u51fd\u6570\u300d\u5c06\u8f93\u51fa\u6620\u5c04\u52300~1\u4e4b\u95f4&#xff0c;\u4ece\u800c\u8868\u793a&#034;\u5c5e\u4e8e\u67d0\u4e00\u7c7b\u7684\u6982\u7387&#034;\u3002<\/p>\n<h4>1. \u6838\u5fc3\u539f\u7406&#xff1a;sigmoid\u51fd\u6570\u7684\u5206\u7c7b<\/h4>\n<li>\n<p>\u5148\u505a\u7ebf\u6027\u56de\u5f52&#xff1a;\u8ba1\u7b97 <img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"30\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081758-697b17b6dccdd.png\" width=\"253\" \/>&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u518d\u901a\u8fc7sigmoid\u51fd\u6570\u6620\u5c04&#xff1a;<img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"48\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081759-697b17b7004a2.png\" width=\"217\" \/><\/p>\n<li>\n<p>\u5f53 P\u22650.5\u00a0\u65f6&#xff0c;\u9884\u6d4b\u4e3a\u7c7b\u522b1&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u5f53 P&lt;0.5 \u65f6&#xff0c;\u9884\u6d4b\u4e3a\u7c7b\u522b0\u3002<\/p>\n<\/li>\n<\/li>\n<p>sigmoid\u51fd\u6570\u7684\u4f5c\u7528\u662f&#034;\u538b\u7f29&#034;&#xff1a;\u65e0\u8bbaz\u662f\u591a\u5927\u7684\u6b63\u6570\u6216\u8d1f\u6570&#xff0c;\u8f93\u51fa\u90fd\u7a33\u5b9a\u57280~1\u4e4b\u95f4&#xff0c;\u5b8c\u7f8e\u5951\u5408&#034;\u6982\u7387&#034;\u7684\u5b9a\u4e49\u3002<\/p>\n<h4>2. \u5b9e\u6218\u6848\u4f8b&#xff1a;\u4fe1\u7528\u5361\u6b3a\u8bc8\u68c0\u6d4b<\/h4>\n<h5>&#xff08;1&#xff09;\u6570\u636e\u9884\u5904\u7406&#xff1a;\u89e3\u51b3&#034;\u6570\u636e\u4e0d\u4e00\u81f4&#034;\u95ee\u9898<\/h5>\n<p>import pandas as pd<br \/>\nfrom sklearn.preprocessing import StandardScaler<\/p>\n<p># 1. \u6570\u636e\u8bfb\u53d6&#xff08;\u5904\u7406\u7f16\u7801\u95ee\u9898&#xff09;<br \/>\ndata &#061; pd.read_csv(&#034;.\/creditcard.csv&#034;, encoding&#061;&#039;utf8&#039;, engine&#061;&#039;python&#039;)<\/p>\n<p># 2. \u6570\u636e\u8131\u654f&#xff1a;\u5df2\u63d0\u524d\u5904\u7406&#xff0c;\u4fdd\u62a4\u7528\u6237\u9690\u79c1<br \/>\n# 3. \u7279\u5f81\u6807\u51c6\u5316&#xff1a;Amount&#xff08;\u4ea4\u6613\u91d1\u989d&#xff09;\u548c\u5176\u4ed6\u7279\u5f81\u91cf\u7ea7\u5dee\u5f02\u5927&#xff0c;\u9700\u5f52\u4e00\u5316<br \/>\nscaler &#061; StandardScaler()<br \/>\ndata[&#039;Amount&#039;] &#061; scaler.fit_transform(data[[&#039;Amount&#039;]])  # Z\u6807\u51c6\u5316&#xff1a;(x-\u5747\u503c)\/\u6807\u51c6\u5dee<\/p>\n<p># 4. \u5220\u9664\u65e0\u5173\u7279\u5f81&#xff1a;Time&#xff08;\u4ea4\u6613\u65f6\u95f4&#xff09;\u5bf9\u5206\u7c7b\u65e0\u610f\u4e49<br \/>\ndata &#061; data.drop([&#039;Time&#039;], axis&#061;1)<\/p>\n<p># \u67e5\u770b\u6570\u636e\u524d5\u884c<br \/>\nprint(data.head()) <\/p>\n<p>\u4e3a\u4ec0\u4e48\u8981\u6807\u51c6\u5316&#xff1f;<\/p>\n<p>\u5982\u679cAmount\u662f&#034;\u4e07\u5143\u7ea7&#034;&#xff0c;\u800c\u5176\u4ed6\u7279\u5f81\u662f&#034;0~1\u7ea7&#034;&#xff0c;\u6a21\u578b\u4f1a\u8fc7\u5ea6\u91cd\u89c6Amount\u7684\u5f71\u54cd\u3002\u6807\u51c6\u5316\u540e\u6240\u6709\u7279\u5f81\u6743\u91cd\u4e00\u81f4&#xff0c;\u6a21\u578b\u66f4\u516c\u5e73\u3002<\/p>\n<h5>&#xff08;2&#xff09;\u53ef\u89c6\u5316&#xff1a;\u67e5\u770b\u6b63\u8d1f\u6837\u672c\u5206\u5e03<\/h5>\n<p>import matplotlib.pyplot as plt<br \/>\nfrom pylab import mpl<\/p>\n<p># \u89e3\u51b3\u4e2d\u6587\u663e\u793a\u95ee\u9898<br \/>\nmpl.rcParams[&#039;font.sans-serif&#039;] &#061; [&#039;Microsoft YaHei&#039;]<br \/>\nmpl.rcParams[&#039;axes.unicode_minus&#039;] &#061; False<\/p>\n<p># \u7edf\u8ba1\u6b63\u8d1f\u6837\u672c\u4e2a\u6570<br \/>\nlabels_count &#061; pd.value_counts(data[&#039;Class&#039;])<br \/>\nprint(labels_count)  # \u8f93\u51fa&#xff1a;0    284315&#xff0c;1     492<\/p>\n<p># \u7ed8\u5236\u67f1\u72b6\u56fe<br \/>\nplt.title(&#034;\u4fe1\u7528\u5361\u4ea4\u6613\u6b63\u8d1f\u6837\u672c\u6570&#034;)<br \/>\nplt.xlabel(&#034;\u7c7b\u522b&#xff08;0&#061;\u6b63\u5e38&#xff0c;1&#061;\u6b3a\u8bc8&#xff09;&#034;)<br \/>\nplt.ylabel(&#034;\u9891\u6570&#034;)<br \/>\nlabels_count.plot(kind&#061;&#039;bar&#039;)<br \/>\nplt.show() <\/p>\n<p>\u8fd9\u91cc\u80fd\u53d1\u73b0\u300c\u6837\u672c\u4e0d\u5747\u8861\u300d&#xff08;\u6b63\u5e38\u4ea4\u6613\u8fdc\u591a\u4e8e\u6b3a\u8bc8&#xff09;&#xff0c;\u8fd9\u4e5f\u662f\u5206\u7c7b\u4efb\u52a1\u4e2d\u5e38\u89c1\u7684\u95ee\u9898\u2014\u2014\u540e\u7eed\u8bc4\u4ef7\u4e0d\u80fd\u53ea\u770b\u51c6\u786e\u7387&#xff0c;\u8fd8\u8981\u770b\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3002<\/p>\n<h5>&#xff08;3&#xff09;\u6a21\u578b\u8bad\u7ec3&#xff1a;\u6b63\u5219\u5316\u9632\u6b62\u8fc7\u62df\u5408<\/h5>\n<p>from sklearn.linear_model import LogisticRegression<br \/>\nfrom sklearn.model_selection import train_test_split<\/p>\n<p># \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<br \/>\nX &#061; data.drop([&#039;Class&#039;], axis&#061;1)<br \/>\ny &#061; data[&#039;Class&#039;]<br \/>\nX_train, X_test, y_train, y_test &#061; train_test_split(X, y, test_size&#061;0.2, random_state&#061;1000)   #\u968f\u673a\u9009\u53d6&#xff0c;\u751f\u6210\u968f\u673a\u79cd\u5b50&#xff0c;\u4fdd\u8bc1\u7ed3\u679c\u4e0d\u53d8<\/p>\n<p># \u521d\u59cb\u5316\u903b\u8f91\u56de\u5f52\u6a21\u578b&#xff1a;C\u662f\u6b63\u5219\u5316\u53c2\u6570&#xff08;C\u8d8a\u5c0f&#xff0c;\u6b63\u5219\u5316\u8d8a\u5f3a&#xff0c;\u9632\u6b62\u8fc7\u62df\u5408&#xff09;<br \/>\nlr &#061; LogisticRegression(C&#061;0.01)<\/p>\n<p># \u8bad\u7ec3\u6a21\u578b<br \/>\nlr.fit(X_train, y_train)<\/p>\n<p># \u6d4b\u8bd5\u96c6\u9884\u6d4b<br \/>\ntest_predicted &#061; lr.predict(X_test) <\/p>\n<p>\u8fd0\u884c\u7ed3\u679c&#xff1a;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"388\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081759-697b17b70f914.png\" width=\"703\" \/><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"431\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081759-697b17b72dcb9.png\" width=\"575\" \/><\/p>\n<h5>&#xff08;4&#xff09;\u6a21\u578b\u8bc4\u4ef7&#xff1a;\u4e0d\u6b62\u770b\u51c6\u786e\u7387<\/h5>\n<p>\u5206\u7c7b\u4efb\u52a1\u4e2d&#xff0c;\u300c\u51c6\u786e\u7387\u300d\u5f80\u5f80\u4e0d\u591f\u7528&#xff08;\u6bd4\u5982\u6837\u672c\u4e0d\u5747\u8861\u65f6&#xff0c;\u6a21\u578b\u5168\u9884\u6d4b\u4e3a0\u4e5f\u80fd\u670999%\u51c6\u786e\u7387&#xff09;&#xff0c;\u300c\u6df7\u6dc6\u77e9\u9635\u300d\u548c\u56db\u5927\u6838\u5fc3\u6307\u6807&#xff1a;<\/p>\n<h6>\u2460 \u6df7\u6dc6\u77e9\u9635<\/h6>\n<table>\n<tbody>\n<tr>\n<td colspan=\"1\" rowspan=\"1\"><\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u9884\u6d4b\u503c&#061;1<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u9884\u6d4b\u503c&#061;0<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u771f\u5b9e\u503c&#061;1<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>TP&#xff08;\u771f\u9633\u6027&#xff09;<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>FN&#xff08;\u5047\u9634\u6027&#xff09;<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u771f\u5b9e\u503c&#061;0<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>FP&#xff08;\u5047\u9633\u6027&#xff09;<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>TN&#xff08;\u771f\u9634\u6027&#xff09;<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ul>\n<li>\n<p>TP&#xff1a;\u771f\u5b9e\u662f1&#xff0c;\u9884\u6d4b\u4e5f\u662f1&#xff1b;<\/p>\n<\/li>\n<li>\n<p>FN&#xff1a;\u771f\u5b9e\u662f1&#xff0c;\u9884\u6d4b\u662f0 &#xff1b;<\/p>\n<\/li>\n<li>\n<p>FP&#xff1a;\u771f\u5b9e\u662f0&#xff0c;\u9884\u6d4b\u662f1 &#xff1b;<\/p>\n<\/li>\n<li>\n<p>TN&#xff1a;\u771f\u5b9e\u662f0&#xff0c;\u9884\u6d4b\u662f0 \u3002<\/p>\n<\/li>\n<\/ul>\n<h6>\u2461 \u56db\u5927\u8bc4\u4ef7\u6307\u6807<\/h6>\n<li>\n<p>\u51c6\u786e\u7387&#xff08;Accuracy&#xff09;&#xff1a;<img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"65\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081759-697b17b768295.png\" width=\"230\" \/>&#xff08;\u6574\u4f53\u9884\u6d4b\u6b63\u786e\u7684\u6bd4\u4f8b&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u7cbe\u786e\u7387&#xff08;Precision&#xff09;&#xff1a;<img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"55\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081759-697b17b77923c.png\" width=\"105\" \/>&#xff08;\u9884\u6d4b\u4e3a1\u7684\u6837\u672c\u4e2d&#xff0c;\u771f\u5b9e\u662f1\u7684\u6bd4\u4f8b&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>\u53ec\u56de\u7387&#xff08;Recall&#xff09;&#xff1a;<img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"58\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081759-697b17b788379.png\" width=\"117\" \/>&#xff08;\u771f\u5b9e\u662f1\u7684\u6837\u672c\u4e2d&#xff0c;\u88ab\u9884\u6d4b\u4e3a1\u7684\u6bd4\u4f8b&#xff09;&#xff1b;<\/p>\n<\/li>\n<li>\n<p>F1-score&#xff1a;<img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"60\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260129081759-697b17b794633.png\" width=\"245\" \/>&#xff08;\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u7684\u8c03\u548c\u5e73\u5747&#xff0c;\u7efc\u5408\u8bc4\u4ef7&#xff09;\u3002<\/p>\n<\/li>\n<p>\u6837\u672c\u4e0d\u5747\u8861\u65f6&#xff0c;\u53ec\u56de\u7387\u548cF1-score\u66f4\u91cd\u8981\u3002<\/p>\n<h4>3. 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