{"id":1084010,"date":"2025-01-08T13:03:23","date_gmt":"2025-01-08T05:03:23","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1084010.html"},"modified":"2025-01-08T13:03:27","modified_gmt":"2025-01-08T05:03:27","slug":"python%e5%a6%82%e4%bd%95%e5%a4%84%e7%90%86%e5%a4%9a%e5%88%86%e7%b1%bb%e5%8f%ac%e5%9b%9e%e7%8e%87-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1084010.html","title":{"rendered":"python\u5982\u4f55\u5904\u7406\u591a\u5206\u7c7b\u53ec\u56de\u7387"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/24194554\/d76982bb-5cb5-4fbf-a7a4-5efbc8047918.webp\" alt=\"python\u5982\u4f55\u5904\u7406\u591a\u5206\u7c7b\u53ec\u56de\u7387\" \/><\/p>\n<p><p> <strong>Python\u5904\u7406\u591a\u5206\u7c7b\u53ec\u56de\u7387\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528sklearn\u5e93\u3001\u4f7f\u7528\u6df7\u6dc6\u77e9\u9635\u3001\u8ba1\u7b97\u6bcf\u7c7b\u7684\u53ec\u56de\u7387\u3001\u7ed8\u5236\u5206\u7c7b\u62a5\u544a\u3002<\/strong>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u5904\u7406\u591a\u5206\u7c7b\u53ec\u56de\u7387\uff0c\u5e76\u4e14\u6df1\u5165\u63a2\u8ba8\u6bcf\u79cd\u65b9\u6cd5\u7684\u5b9e\u73b0\u7ec6\u8282\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u4f7f\u7528sklearn\u5e93<\/p>\n<\/p>\n<p><p>Python\u7684sklearn\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u8bc4\u4f30\u6307\u6807\uff0c\u5176\u4e2d\u5305\u62ec\u591a\u5206\u7c7b\u53ec\u56de\u7387\u7684\u8ba1\u7b97\u3002\u4f7f\u7528sklearn\u5e93\u4e2d\u7684<code>classification_report<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u76f4\u63a5\u751f\u6210\u5305\u62ec\u53ec\u56de\u7387\u5728\u5185\u7684\u8be6\u7ec6\u5206\u7c7b\u62a5\u544a\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5bfc\u5165\u6240\u9700\u5e93<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">from sklearn.metrics import classification_report<\/p>\n<p>import numpy as np<\/p>\n<p>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.datasets import load_iris<\/p>\n<p>from sklearn.linear_model import LogisticRegression<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u52a0\u8f7d\u6570\u636e\u5e76\u8bad\u7ec3\u6a21\u578b<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\"># \u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n<p>data = load_iris()<\/p>\n<p>X = data.data<\/p>\n<p>y = data.target<\/p>\n<h2><strong>\u62c6\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/p>\n<h2><strong>\u8bad\u7ec3\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n<p>model = LogisticRegression(max_iter=200)<\/p>\n<p>model.fit(X_train, y_train)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u751f\u6210\u5206\u7c7b\u62a5\u544a<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\"># \u9884\u6d4b\u6d4b\u8bd5\u96c6<\/p>\n<p>y_pred = model.predict(X_test)<\/p>\n<h2><strong>\u751f\u6210\u5e76\u6253\u5370\u5206\u7c7b\u62a5\u544a<\/strong><\/h2>\n<p>report = classification_report(y_test, y_pred)<\/p>\n<p>print(report)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c<code>classification_report<\/code>\u51fd\u6570\u4f1a\u8f93\u51fa\u6bcf\u4e2a\u7c7b\u522b\u7684\u7cbe\u786e\u5ea6\u3001\u53ec\u56de\u7387\u3001F1\u5206\u6570\u53ca\u5176\u5b8f\u89c2\u5e73\u5747\u548c\u52a0\u6743\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n<p><p>\u4e8c\u3001\u4f7f\u7528\u6df7\u6dc6\u77e9\u9635<\/p>\n<\/p>\n<p><p>\u6df7\u6dc6\u77e9\u9635\u662f\u8bc4\u4ef7\u5206\u7c7b\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u5de5\u5177\uff0c\u901a\u8fc7\u6df7\u6dc6\u77e9\u9635\u53ef\u4ee5\u76f4\u89c2\u4e86\u89e3\u6bcf\u4e2a\u7c7b\u522b\u7684\u9884\u6d4b\u60c5\u51b5\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u7c7b\u522b\u7684\u53ec\u56de\u7387\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5bfc\u5165\u6240\u9700\u5e93<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">from sklearn.metrics import confusion_matrix<\/p>\n<p>import seaborn as sns<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u8ba1\u7b97\u5e76\u7ed8\u5236\u6df7\u6dc6\u77e9\u9635<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\"># \u8ba1\u7b97\u6df7\u6dc6\u77e9\u9635<\/p>\n<p>cm = confusion_matrix(y_test, y_pred)<\/p>\n<h2><strong>\u7ed8\u5236\u6df7\u6dc6\u77e9\u9635<\/strong><\/h2>\n<p>sns.heatmap(cm, annot=True, fmt=&#39;d&#39;, cmap=&#39;Blues&#39;)<\/p>\n<p>plt.xlabel(&#39;Predicted&#39;)<\/p>\n<p>plt.ylabel(&#39;Actual&#39;)<\/p>\n<p>plt.title(&#39;Confusion Matrix&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"3\">\n<li>\u8ba1\u7b97\u6bcf\u7c7b\u7684\u53ec\u56de\u7387<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\"># \u8ba1\u7b97\u6bcf\u4e2a\u7c7b\u522b\u7684\u53ec\u56de\u7387<\/p>\n<p>recall_per_class = cm.diagonal() \/ cm.sum(axis=1)<\/p>\n<p>print(&quot;Recall per class: &quot;, recall_per_class)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u6df7\u6dc6\u77e9\u9635\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u6a21\u578b\u7684\u5206\u7c7b\u6548\u679c\uff0c\u5e76\u901a\u8fc7\u5bf9\u89d2\u5143\u7d20\u548c\u6bcf\u884c\u5143\u7d20\u7684\u6bd4\u503c\u8ba1\u7b97\u51fa\u6bcf\u4e2a\u7c7b\u522b\u7684\u53ec\u56de\u7387\u3002<\/p>\n<\/p>\n<p><p>\u4e09\u3001\u8ba1\u7b97\u6bcf\u7c7b\u7684\u53ec\u56de\u7387<\/p>\n<\/p>\n<p><p>\u53ec\u56de\u7387\u662f\u6307\u5728\u6240\u6709\u5b9e\u9645\u4e3a\u6b63\u7684\u6837\u672c\u4e2d\uff0c\u88ab\u6b63\u786e\u9884\u6d4b\u4e3a\u6b63\u7684\u6bd4\u4f8b\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5bf9\u4e8e\u591a\u5206\u7c7b\u95ee\u9898\uff0c\u53ec\u56de\u7387\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u516c\u5f0f\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n<p><p>[ \\text{\u53ec\u56de\u7387} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}} ]<\/p>\n<\/p>\n<p><p>\u5176\u4e2d\uff0cTP\u8868\u793a\u771f\u9633\u6027\uff0cFN\u8868\u793a\u5047\u9634\u6027\u3002<\/p>\n<\/p>\n<ol>\n<li>\u8ba1\u7b97\u6bcf\u7c7b\u7684\u53ec\u56de\u7387<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\"># \u8ba1\u7b97\u6bcf\u4e2a\u7c7b\u522b\u7684\u53ec\u56de\u7387<\/p>\n<p>recall_per_class = cm.diagonal() \/ cm.sum(axis=1)<\/p>\n<p>for i, recall in enumerate(recall_per_class):<\/p>\n<p>    print(f&quot;Recall for class {i}: {recall}&quot;)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4e0a\u9762\u7684\u8ba1\u7b97\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5f97\u5230\u6bcf\u4e2a\u7c7b\u522b\u7684\u53ec\u56de\u7387\uff0c\u4ece\u800c\u8bc4\u4f30\u6a21\u578b\u5728\u4e0d\u540c\u7c7b\u522b\u4e0a\u7684\u5206\u7c7b\u6548\u679c\u3002<\/p>\n<\/p>\n<p><p>\u56db\u3001\u7ed8\u5236\u5206\u7c7b\u62a5\u544a<\/p>\n<\/p>\n<p><p>\u5728\u5904\u7406\u591a\u5206\u7c7b\u95ee\u9898\u65f6\uff0c\u7ed8\u5236\u5206\u7c7b\u62a5\u544a\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u6bcf\u4e2a\u7c7b\u522b\u7684\u5206\u7c7b\u6027\u80fd\u3002<\/p>\n<\/p>\n<ol>\n<li>\u5bfc\u5165\u6240\u9700\u5e93<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\">from sklearn.metrics import plot_confusion_matrix<\/p>\n<p><\/code><\/pre>\n<\/p>\n<ol start=\"2\">\n<li>\u7ed8\u5236\u5206\u7c7b\u62a5\u544a<\/li>\n<\/ol>\n<p><pre><code class=\"language-python\"># \u7ed8\u5236\u6df7\u6dc6\u77e9\u9635<\/p>\n<p>plot_confusion_matrix(model, X_test, y_test, display_labels=data.target_names, cmap=plt.cm.Blues)<\/p>\n<p>plt.title(&#39;Confusion Matrix&#39;)<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u7ed8\u5236\u6df7\u6dc6\u77e9\u9635\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u6a21\u578b\u5728\u4e0d\u540c\u7c7b\u522b\u4e0a\u7684\u5206\u7c7b\u6548\u679c\uff0c\u5e76\u7ed3\u5408\u4e0a\u9762\u7684\u5206\u7c7b\u62a5\u544a\uff0c\u5168\u9762\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<p><p><strong>\u603b\u7ed3<\/strong><\/p>\n<\/p>\n<p><p>\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86Python\u5904\u7406\u591a\u5206\u7c7b\u53ec\u56de\u7387\u7684\u56db\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528sklearn\u5e93\u3001\u4f7f\u7528\u6df7\u6dc6\u77e9\u9635\u3001\u8ba1\u7b97\u6bcf\u7c7b\u7684\u53ec\u56de\u7387\u3001\u7ed8\u5236\u5206\u7c7b\u62a5\u544a\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5168\u9762\u8bc4\u4f30\u6a21\u578b\u5728\u4e0d\u540c\u7c7b\u522b\u4e0a\u7684\u5206\u7c7b\u6548\u679c\uff0c\u4ece\u800c\u4f18\u5316\u6a21\u578b\u6027\u80fd\u3002\u5e0c\u671b\u672c\u6587\u80fd\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u591a\u5206\u7c7b\u53ec\u56de\u7387\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528<code>scikit-learn<\/code>\u5e93\u4e2d\u7684<code>recall_score<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u591a\u5206\u7c7b\u53ec\u56de\u7387\u3002\u8be5\u51fd\u6570\u5141\u8bb8\u6307\u5b9a<code>average<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u9009\u62e9<code>micro<\/code>\u3001<code>macro<\/code>\u6216<code>weighted<\/code>\uff0c\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\u6765\u8ba1\u7b97\u53ec\u56de\u7387\u3002\u4f8b\u5982\uff0c<code>average=&#39;macro&#39;<\/code>\u4f1a\u8ba1\u7b97\u6bcf\u4e2a\u7c7b\u522b\u7684\u53ec\u56de\u7387\u5e76\u53d6\u5e73\u5747\uff0c<code>average=&#39;weighted&#39;<\/code>\u4f1a\u8003\u8651\u6bcf\u4e2a\u7c7b\u522b\u7684\u6837\u672c\u6570\u91cf\u8fdb\u884c\u52a0\u6743\u3002<\/p>\n<p><strong>\u5728\u591a\u5206\u7c7b\u95ee\u9898\u4e2d\uff0c\u53ec\u56de\u7387\u4e0e\u5176\u4ed6\u8bc4\u4f30\u6307\u6807\u6709\u4f55\u5173\u7cfb\uff1f<\/strong><br \/>\u53ec\u56de\u7387\u662f\u8861\u91cf\u6a21\u578b\u8bc6\u522b\u6b63\u7c7b\u6837\u672c\u80fd\u529b\u7684\u4e00\u4e2a\u91cd\u8981\u6307\u6807\uff0c\u4f46\u5728\u591a\u5206\u7c7b\u95ee\u9898\u4e2d\uff0c\u9664\u4e86\u53ec\u56de\u7387\u5916\uff0c\u8fd8\u9700\u8981\u5173\u6ce8\u7cbe\u786e\u7387\u3001F1-score\u7b49\u6307\u6807\u3002\u7cbe\u786e\u7387\u5173\u6ce8\u7684\u662f\u6a21\u578b\u9884\u6d4b\u7684\u6b63\u7c7b\u6837\u672c\u4e2d\u6709\u591a\u5c11\u662f\u771f\u6b63\u7684\u6b63\u7c7b\uff0c\u800cF1-score\u5219\u662f\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u7684\u8c03\u548c\u5e73\u5747\uff0c\u7efc\u5408\u8003\u8651\u4e86\u4e24\u8005\u7684\u5e73\u8861\u3002\u56e0\u6b64\uff0c\u5728\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u65f6\uff0c\u5efa\u8bae\u540c\u65f6\u67e5\u770b\u8fd9\u4e9b\u6307\u6807\u3002<\/p>\n<p><strong>\u5982\u4f55\u5904\u7406\u4e0d\u5e73\u8861\u7c7b\u522b\u5bf9\u591a\u5206\u7c7b\u53ec\u56de\u7387\u7684\u5f71\u54cd\uff1f<\/strong><br \/>\u5728\u591a\u5206\u7c7b\u95ee\u9898\u4e2d\uff0c\u5982\u679c\u67d0\u4e9b\u7c7b\u522b\u7684\u6837\u672c\u6570\u91cf\u8fdc\u5c11\u4e8e\u5176\u4ed6\u7c7b\u522b\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u53ec\u56de\u7387\u504f\u4f4e\u3002\u4e3a\u4e86\u5e94\u5bf9\u8fd9\u79cd\u60c5\u51b5\uff0c\u53ef\u4ee5\u4f7f\u7528\u8fc7\u91c7\u6837\u6216\u6b20\u91c7\u6837\u65b9\u6cd5\u6765\u5e73\u8861\u7c7b\u522b\u5206\u5e03\uff0c\u6216\u8005\u4f7f\u7528\u60e9\u7f5a\u6027\u635f\u5931\u51fd\u6570\u6765\u63d0\u9ad8\u6a21\u578b\u5bf9\u5c11\u6570\u7c7b\u522b\u7684\u654f\u611f\u6027\u3002\u6b64\u5916\uff0c\u8c03\u6574\u9608\u503c\u4e5f\u53ef\u4ee5\u5e2e\u52a9\u6539\u5584\u5c11\u6570\u7c7b\u522b\u7684\u53ec\u56de\u7387\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u6709\u6548\u5730\u63d0\u5347\u6a21\u578b\u5728\u4e0d\u5e73\u8861\u6570\u636e\u96c6\u4e0a\u7684\u8868\u73b0\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5904\u7406\u591a\u5206\u7c7b\u53ec\u56de\u7387\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528sklearn\u5e93\u3001\u4f7f\u7528\u6df7\u6dc6\u77e9\u9635\u3001\u8ba1\u7b97\u6bcf\u7c7b\u7684\u53ec\u56de\u7387\u3001\u7ed8\u5236\u5206\u7c7b\u62a5\u544a\u3002\u672c\u6587 [&hellip;]","protected":false},"author":3,"featured_media":1084019,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1084010"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1084010"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1084010\/revisions"}],"predecessor-version":[{"id":1084020,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1084010\/revisions\/1084020"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1084019"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1084010"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1084010"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1084010"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}