{"id":953261,"date":"2024-12-27T01:41:35","date_gmt":"2024-12-26T17:41:35","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/953261.html"},"modified":"2024-12-27T01:41:36","modified_gmt":"2024-12-26T17:41:36","slug":"pr%e6%9b%b2%e7%ba%bf%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6-python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/953261.html","title":{"rendered":"pr\u66f2\u7ebf\u5982\u4f55\u7ed8\u5236 python"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25090825\/9074874e-d30e-49eb-b0bd-62f61a419d11.webp\" alt=\"pr\u66f2\u7ebf\u5982\u4f55\u7ed8\u5236 python\" \/><\/p>\n<p><p> <strong>PR\u66f2\u7ebf\u7684\u7ed8\u5236\u5728Python\u4e2d\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a\u4f7f\u7528sklearn\u5e93\u8ba1\u7b97\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u3001\u5229\u7528matplotlib\u5e93\u8fdb\u884c\u7ed8\u5236\u3001\u8c03\u8bd5\u548c\u4f18\u5316\u6a21\u578b\u4ee5\u63d0\u9ad8\u6027\u80fd\u3002<\/strong>\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236PR\u66f2\u7ebf\uff0c\u5e76\u8be6\u7ec6\u8ba8\u8bba\u4f7f\u7528sklearn\u548cmatplotlib\u5e93\u7684\u5177\u4f53\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u4f7f\u7528SKLEARN\u8ba1\u7b97\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387<\/p>\n<\/p>\n<p><p>PR\u66f2\u7ebf\uff08Precision-Recall Curve\uff09\u662f\u7528\u4e8e\u8bc4\u4f30\u4e8c\u5206\u7c7b\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u5de5\u5177\u3002\u5b83\u901a\u8fc7\u5c55\u793a\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\uff08Precision\uff09\u548c\u53ec\u56de\u7387\uff08Recall\uff09\u4e4b\u95f4\u7684\u53d8\u5316\u5173\u7cfb\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6a21\u578b\u7684\u4f18\u52a3\u3002<\/p>\n<\/p>\n<ol>\n<li><strong>\u6570\u636e\u51c6\u5907\u4e0e\u6a21\u578b\u8bad\u7ec3<\/strong><\/li>\n<\/ol>\n<p><p>\u5728\u7ed8\u5236PR\u66f2\u7ebf\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u6570\u636e\u5e76\u8bad\u7ec3\u4e00\u4e2a\u5206\u7c7b\u6a21\u578b\u3002\u901a\u5e38\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u4f1a\u5206\u5272\u6570\u636e\u96c6\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u4fbf\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\u4ee5sklearn\u5e93\u4e2d\u7684Logistic Regression\u4e3a\u4f8b\uff0c\u9996\u5148\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.model_selection import tr<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n_test_split<\/p>\n<p>from sklearn.linear_model import LogisticRegression<\/p>\n<p>from sklearn.datasets import make_classification<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u751f\u6210\u4e00\u4e2a\u6a21\u62df\u6570\u636e\u96c6\uff0c\u5e76\u5c06\u5176\u5206\u5272\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)<\/p>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u7136\u540e\u8bad\u7ec3\u4e00\u4e2aLogistic Regression\u6a21\u578b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">model = LogisticRegression()<\/p>\n<p>model.fit(X_train, y_train)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li><strong>\u8ba1\u7b97\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387<\/strong><\/li>\n<\/ol>\n<p><p>\u4f7f\u7528sklearn\u5e93\u7684<code>precision_recall_curve<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.metrics import precision_recall_curve<\/p>\n<p>y_scores = model.decision_function(X_test)<\/p>\n<p>precision, recall, thresholds = precision_recall_curve(y_test, y_scores)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u8fd9\u91cc\uff0c<code>decision_function<\/code>\u65b9\u6cd5\u8fd4\u56de\u7684\u662f\u6837\u672c\u8ddd\u79bb\u51b3\u7b56\u8fb9\u754c\u7684\u5206\u6570\uff0c\u9002\u7528\u4e8e\u652f\u6301\u5411\u91cf\u673a\u548c\u903b\u8f91\u56de\u5f52\u7b49\u6a21\u578b\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528MATPLOTLIB\u7ed8\u5236PR\u66f2\u7ebf<\/p>\n<\/p>\n<ol>\n<li><strong>\u7ed8\u5236PR\u66f2\u7ebf<\/strong><\/li>\n<\/ol>\n<p><p>\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236PR\u66f2\u7ebf\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u6a21\u578b\u5728\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import matplotlib.pyplot as plt<\/p>\n<p>plt.figure()<\/p>\n<p>plt.plot(recall, precision, marker=&#39;.&#39;)<\/p>\n<p>plt.title(&#39;Precision-Recall Curve&#39;)<\/p>\n<p>plt.xlabel(&#39;Recall&#39;)<\/p>\n<p>plt.ylabel(&#39;Precision&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li><strong>\u89e3\u91caPR\u66f2\u7ebf<\/strong><\/li>\n<\/ol>\n<p><p>\u5728PR\u66f2\u7ebf\u4e2d\uff0c\u6a2a\u8f74\u4e3a\u53ec\u56de\u7387\uff0c\u7eb5\u8f74\u4e3a\u7cbe\u786e\u7387\u3002<strong>\u7406\u60f3\u7684\u6a21\u578b\u5176PR\u66f2\u7ebf\u5e94\u5c3d\u53ef\u80fd\u63a5\u8fd1\u5de6\u4e0a\u89d2\uff0c\u8fd9\u610f\u5473\u7740\u5728\u4fdd\u6301\u8f83\u9ad8\u7cbe\u786e\u7387\u7684\u540c\u65f6\u5177\u5907\u8f83\u9ad8\u7684\u53ec\u56de\u7387<\/strong>\u3002\u5982\u679cPR\u66f2\u7ebf\u504f\u5411\u53f3\u4e0b\u89d2\uff0c\u5219\u8bf4\u660e\u6a21\u578b\u5728\u67d0\u4e9b\u9608\u503c\u4e0b\u8868\u73b0\u4e0d\u4f73\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u8c03\u8bd5\u548c\u4f18\u5316\u6a21\u578b<\/p>\n<\/p>\n<ol>\n<li><strong>\u8c03\u6574\u9608\u503c<\/strong><\/li>\n<\/ol>\n<p><p>\u901a\u8fc7\u89c2\u5bdfPR\u66f2\u7ebf\uff0c\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u4e00\u4e2a\u5408\u9002\u7684\u9608\u503c\u6765\u5e73\u8861\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u3002\u4f8b\u5982\uff0c\u5728\u67d0\u4e9b\u5e94\u7528\u573a\u666f\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u66f4\u6ce8\u91cd\u53ec\u56de\u7387\u800c\u975e\u7cbe\u786e\u7387\uff0c\u6b64\u65f6\u53ef\u4ee5\u9009\u62e9\u4e00\u4e2a\u8f83\u4f4e\u7684\u9608\u503c\u4ee5\u63d0\u9ad8\u53ec\u56de\u7387\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">optimal_idx = (precision + recall).argmax()<\/p>\n<p>optimal_threshold = thresholds[optimal_idx]<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li><strong>\u8c03\u6574\u6a21\u578b\u53c2\u6570<\/strong><\/li>\n<\/ol>\n<p><p>\u4e0d\u540c\u7684\u6a21\u578b\u53c2\u6570\u53ef\u80fd\u5bfc\u81f4\u4e0d\u540c\u7684PR\u66f2\u7ebf\uff0c\u56e0\u6b64\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u6765\u4f18\u5316\u6a21\u578b\u6027\u80fd\u3002\u4ee5Logistic Regression\u4e3a\u4f8b\uff0c\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574<code>C<\/code>\u53c2\u6570\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">model = LogisticRegression(C=0.5)<\/p>\n<p>model.fit(X_train, y_train)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li><strong>\u589e\u52a0\u7279\u5f81\u6216\u6570\u636e\u91cf<\/strong><\/li>\n<\/ol>\n<p><p>\u589e\u52a0\u8bad\u7ec3\u6570\u636e\u91cf\u6216\u7279\u5f81\u6570\u53ef\u80fd\u4f1a\u6539\u5584\u6a21\u578b\u6027\u80fd\uff0c\u4ece\u800c\u63d0\u5347PR\u66f2\u7ebf\u8868\u73b0\u3002\u5728\u6570\u636e\u91cf\u8f83\u5c11\u6216\u7279\u5f81\u8f83\u5c11\u7684\u60c5\u51b5\u4e0b\uff0c\u6a21\u578b\u53ef\u80fd\u65e0\u6cd5\u5145\u5206\u5b66\u4e60\u6570\u636e\u7684\u7279\u5f81\uff0c\u5bfc\u81f4\u6027\u80fd\u4e0b\u964d\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u6df1\u5165\u7406\u89e3PR\u66f2\u7ebf\u4e0eAUC\u6307\u6807<\/p>\n<\/p>\n<ol>\n<li><strong>PR\u66f2\u7ebf\u4e0eROC\u66f2\u7ebf\u7684\u533a\u522b<\/strong><\/li>\n<\/ol>\n<p><p>PR\u66f2\u7ebf\u4e0eROC\u66f2\u7ebf\uff08Receiver Operating Characteristic Curve\uff09\u90fd\u662f\u8bc4\u4f30\u4e8c\u5206\u7c7b\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u5de5\u5177\u3002PR\u66f2\u7ebf\u9002\u7528\u4e8e\u4e0d\u5e73\u8861\u7684\u6570\u636e\u96c6\uff0c\u56e0\u4e3a\u5b83\u5173\u6ce8\u7684\u662f\u6b63\u7c7b\u6837\u672c\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\uff0c\u800cROC\u66f2\u7ebf\u5219\u4fa7\u91cd\u4e8e\u6574\u4f53\u7684\u771f\u9633\u6027\u7387\u548c\u5047\u9633\u6027\u7387\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li><strong>AUC-PR\u6307\u6807<\/strong><\/li>\n<\/ol>\n<p><p>AUC-PR\uff08Area Under the Precision-Recall Curve\uff09\u662fPR\u66f2\u7ebf\u4e0b\u65b9\u9762\u79ef\u7684\u5ea6\u91cf\u3002<strong>AUC-PR\u503c\u8d8a\u9ad8\uff0c\u8868\u793a\u6a21\u578b\u5728\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u8868\u73b0\u8d8a\u597d<\/strong>\u3002\u5728\u4e0d\u5e73\u8861\u6570\u636e\u96c6\u4e0a\uff0cAUC-PR\u901a\u5e38\u6bd4AUC-ROC\u66f4\u5177\u4ee3\u8868\u6027\u3002<\/p>\n<\/p>\n<ol start=\"3\">\n<li><strong>\u4f7f\u7528SKLEARN\u8ba1\u7b97AUC-PR<\/strong><\/li>\n<\/ol>\n<p><p>\u5728Python\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528sklearn\u5e93\u7684<code>average_precision_score<\/code>\u51fd\u6570\u6765\u8ba1\u7b97AUC-PR\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.metrics import average_precision_score<\/p>\n<p>auc_pr = average_precision_score(y_test, y_scores)<\/p>\n<p>print(f&#39;AUC-PR: {auc_pr:.2f}&#39;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u4e94\u3001\u5e94\u7528\u5b9e\u4f8b\u4e0e\u5b9e\u8df5<\/p>\n<\/p>\n<ol>\n<li><strong>\u6848\u4f8b\u5206\u6790\uff1a\u7535\u5b50\u5546\u52a1\u4e2d\u7684\u6b3a\u8bc8\u68c0\u6d4b<\/strong><\/li>\n<\/ol>\n<p><p>\u5728\u7535\u5b50\u5546\u52a1\u5e73\u53f0\u4e0a\uff0c\u6b3a\u8bc8\u68c0\u6d4b\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u4e8c\u5206\u7c7b\u95ee\u9898\u3002\u7531\u4e8e\u6b3a\u8bc8\u4ea4\u6613\u901a\u5e38\u662f\u5c11\u6570\uff0c\u56e0\u6b64\u6570\u636e\u96c6\u901a\u5e38\u662f\u4e0d\u5e73\u8861\u7684\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0cPR\u66f2\u7ebf\u548cAUC-PR\u6307\u6807\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8bc4\u4f30\u6a21\u578b\u5728\u68c0\u6d4b\u6b3a\u8bc8\u4ea4\u6613\u65f6\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<ol start=\"2\">\n<li><strong>\u6848\u4f8b\u5206\u6790\uff1a\u533b\u7597\u8bca\u65ad\u4e2d\u7684\u75be\u75c5\u9884\u6d4b<\/strong><\/li>\n<\/ol>\n<p><p>\u5728\u533b\u7597\u8bca\u65ad\u4e2d\uff0c\u9884\u6d4b\u67d0\u79cd\u75be\u75c5\u662f\u5426\u5b58\u5728\u4e5f\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u4e8c\u5206\u7c7b\u95ee\u9898\u3002\u7531\u4e8e\u67d0\u4e9b\u75be\u75c5\u7684\u53d1\u751f\u7387\u8f83\u4f4e\uff0c\u6570\u636e\u96c6\u53ef\u80fd\u5b58\u5728\u4e0d\u5e73\u8861\u3002\u5728\u8fd9\u79cd\u80cc\u666f\u4e0b\uff0c\u4f7f\u7528PR\u66f2\u7ebf\u53ef\u4ee5\u66f4\u597d\u5730\u8bc4\u4f30\u6a21\u578b\u5728\u68c0\u6d4b\u75be\u75c5\u65f6\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u3002<\/p>\n<\/p>\n<ol start=\"3\">\n<li><strong>\u8c03\u8bd5\u4e0e\u4f18\u5316\u7b56\u7565<\/strong><\/li>\n<\/ol>\n<p><p>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u6a21\u578b\u53c2\u6570\u3001\u589e\u52a0\u6570\u636e\u91cf\u3001\u9009\u62e9\u5408\u9002\u7684\u7279\u5f81\u7b49\u65b9\u5f0f\u6765\u4f18\u5316\u6a21\u578b\u6027\u80fd\u3002\u901a\u8fc7\u89c2\u5bdfPR\u66f2\u7ebf\u548cAUC-PR\u6307\u6807\u7684\u53d8\u5316\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6a21\u578b\u7684\u4f18\u52a3\u5e76\u505a\u51fa\u76f8\u5e94\u8c03\u6574\u3002<\/p>\n<\/p>\n<p><p>\u603b\u7ed3\uff0c\u7ed8\u5236PR\u66f2\u7ebf\u662f\u8bc4\u4f30\u4e8c\u5206\u7c7b\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u624b\u6bb5\u3002\u901a\u8fc7\u8ba1\u7b97\u4e0d\u540c\u9608\u503c\u4e0b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\uff0c\u5e76\u4f7f\u7528matplotlib\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u6211\u4eec\u53ef\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u6a21\u578b\u7684\u6027\u80fd\u3002\u6b64\u5916\uff0c\u901a\u8fc7\u8c03\u6574\u9608\u503c\u3001\u4f18\u5316\u6a21\u578b\u53c2\u6570\u7b49\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u5347\u6a21\u578b\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u8868\u73b0\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528PR\u66f2\u7ebf\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236PR\u66f2\u7ebf\uff1f<\/strong><br 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