{"id":174143,"date":"2024-05-08T18:25:49","date_gmt":"2024-05-08T10:25:49","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/174143.html"},"modified":"2024-05-08T18:25:56","modified_gmt":"2024-05-08T10:25:56","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e9%9d%9e%e7%ba%bf%e6%80%a7%e5%86%b3%e7%ad%96%e8%be%b9%e7%95%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/174143.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/27050310\/80546f8b-6b09-4e19-b306-2115cc25f178.webp\" alt=\"python\u5982\u4f55\u7ed8\u5236\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\" \/><\/p>\n<p><p>\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\u662f<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u5206\u7c7b\u95ee\u9898\u4e2d\u7684\u4e00\u4e2a\u5173\u952e\u6982\u5ff5\uff0c\u5b83\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u66f4\u51c6\u786e\u5730\u5206\u9694\u5e76\u4e0d\u662f\u7ebf\u6027\u53ef\u5206\u7684\u6570\u636e\u3002<strong>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Scikit-learn\u5e93\u7ed3\u5408\u56fe\u5f62\u5e93\u5982Matplotlib\u6765\u7ed8\u5236\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c<\/strong>\u3002Scikit-learn\u63d0\u4f9b\u591a\u79cd\u53ef\u4ee5\u62df\u5408\u975e\u7ebf\u6027\u6570\u636e\u7684\u6a21\u578b\uff0c\u4f8b\u5982\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u7684\u975e\u7ebf\u6027\u6838\u3001\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u3001\u68af\u5ea6\u63d0\u5347\u6811\u3001\u795e\u7ecf\u7f51\u7edc\u7b49\u3002<\/p>\n<\/p>\n<p><p>\u5177\u4f53\u6765\u8bf4\uff0c\u4f7f\u7528\u8fd9\u4e9b\u6a21\u578b\u5e76\u7ed3\u5408Matplotlib\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u7ed8\u5236\u51fa\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\uff1a<\/p>\n<\/p>\n<ol>\n<li>\u5229\u7528Scikit-learn\u5efa\u7acb\u6a21\u578b\u5e76\u8bad\u7ec3\u6570\u636e\u96c6\u3002<\/li>\n<li>\u521b\u5efa\u4e00\u4e2a\u7f51\u683c\uff0c\u8986\u76d6\u6570\u636e\u7684\u6574\u4e2a\u7279\u5f81\u7a7a\u95f4\u3002<\/li>\n<li>\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u7f51\u683c\u4e2d\u6bcf\u4e2a\u70b9\u7684\u6807\u7b7e\u8fdb\u884c\u9884\u6d4b\u3002<\/li>\n<li>\u5c06\u9884\u6d4b\u7ed3\u679c\u7528\u989c\u8272\u7f16\u7801\uff0c\u753b\u5728\u56fe\u4e0a\uff0c\u5f62\u6210\u4e00\u4e2a\u989c\u8272\u533a\u57df\u7684\u56fe\u5c42\uff0c\u4ece\u800c\u663e\u793a\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\u3002<\/li>\n<\/ol>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5b9e\u73b0\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6765\u51c6\u786e\u5730\u7ed8\u5236\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u6570\u636e\u51c6\u5907\u4e0e\u6a21\u578b\u9009\u62e9<\/h3>\n<\/p>\n<p><p>\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u8fd9\u901a\u5e38\u6d89\u53ca\u5230\u6570\u636e\u7684\u52a0\u8f7d\u4e0e\u9884\u5904\u7406\u3002\u5982\u679c\u6570\u636e\u4e0d\u662f\u7ebf\u6027\u53ef\u5206\u7684\uff0c\u6211\u4eec\u53ef\u9009\u62e9\u9002\u5408\u7684\u975e\u7ebf\u6027\u6a21\u578b\u5982SVM\uff08\u5e26\u975e\u7ebf\u6027\u6838\u51fd\u6570\uff09\u3001\u51b3\u7b56\u6811\u7b49\u3002<\/p>\n<\/p>\n<p><h4>SVM\u6a21\u578b<\/h4>\n<\/p>\n<p><p>\u5bf9\u4e8eSVM\uff0c\u5f53\u6211\u4eec\u9009\u62e9RBF\uff08\u5f84\u5411\u57fa\u51fd\u6570\uff09\u6216\u591a\u9879\u5f0f\u6838\u65f6\uff0c\u5b83\u5c31\u80fd\u591f\u5904\u7406\u975e\u7ebf\u6027\u7684\u6570\u636e\u3002\u6211\u4eec\u4f7f\u7528Scikit-learn\u4e2d\u7684<code>SVC<\/code>\u7c7b\uff0c\u5e76\u8bbe\u7f6e<code>kernel<\/code>\u53c2\u6570\u3002<\/p>\n<\/p>\n<p><h4>\u51b3\u7b56\u6811\u6a21\u578b<\/h4>\n<\/p>\n<p><p>\u51b3\u7b56\u6811\u901a\u8fc7\u9012\u5f52\u5730\u5212\u5206\u6570\u636e\u6765\u6784\u5efa\u4e00\u4e2a\u6811\u5f62\u6a21\u578b\u3002\u6bcf\u4e00\u6b21\u6570\u636e\u7684\u5212\u5206\u90fd\u662f\u57fa\u4e8e\u4f7f\u5f97\u4fe1\u606f\u589e\u76ca\u6700\u5927\u5316\u7684\u7279\u5f81\uff0c\u8fd9\u901a\u5e38\u80af\u5b9a\u4e0d\u4f1a\u662f\u7ebf\u6027\u5212\u5206\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u521b\u5efa\u7f51\u683c\u4ee5\u53ca\u6a21\u578b\u8bad\u7ec3<\/h3>\n<\/p>\n<p><p>\u5728\u6a21\u578b\u9009\u62e9\u540e\uff0c\u6211\u4eec\u63a5\u7740\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u7f51\u683c\uff0c\u7528\u6765\u6620\u5c04\u6574\u4e2a\u7279\u5f81\u7a7a\u95f4\u3002<\/p>\n<\/p>\n<p><h4>\u751f\u6210\u7f51\u683c<\/h4>\n<\/p>\n<p><p>\u5229\u7528<code>numpy<\/code>\u7684<code>meshgrid<\/code>\u51fd\u6570\u3001\u7ed3\u5408\u7279\u5f81\u7a7a\u95f4\u4e2d\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\uff0c\u6211\u4eec\u80fd\u591f\u751f\u6210\u4e00\u4e2a\u5bc6\u96c6\u7684\u70b9\u9635\u7f51\u683c\u3002\u8fd9\u4e2a\u7f51\u683c\u5305\u62ec\u4e86\u6211\u4eec\u60f3\u8981\u4f5c\u51fa\u9884\u6d4b\u7684\u6574\u4e2a\u7279\u5f81\u533a\u57df\u3002<\/p>\n<\/p>\n<p><h4>\u6a21\u578b\u8bad\u7ec3<\/h4>\n<\/p>\n<p><p>\u7528\u6211\u4eec\u9009\u62e9\u7684\u6a21\u578b\u5bf9\u8bad\u7ec3\u6570\u636e\u8fdb\u884c\u62df\u5408\u3002\u786e\u4fdd\u6a21\u578b\u6709\u8db3\u591f\u7684\u5f39\u6027\u6765\u6355\u6349\u6570\u636e\u7684\u975e\u7ebf\u6027\u5173\u7cfb\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u9884\u6d4b\u4e0e\u7ed8\u56fe<\/h3>\n<\/p>\n<p><p>\u4e00\u65e6\u6a21\u578b\u88ab\u8bad\u7ec3\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5bf9\u7f51\u683c\u4e2d\u7684\u6bcf\u4e00\u70b9\u8fdb\u884c\u9884\u6d4b\uff0c\u5e76\u5c06\u8fd9\u4e9b\u9884\u6d4b\u7ed3\u679c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n<p><h4>\u9884\u6d4b\u7f51\u683c\u70b9<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u7f51\u683c\u4e2d\u7684\u6bcf\u4e2a\u70b9\u8fdb\u884c\u9884\u6d4b\u6765\u786e\u5b9a\u8fd9\u4e9b\u70b9\u5e94\u5c5e\u4e8e\u54ea\u4e2a\u5206\u7c7b\u3002<\/p>\n<\/p>\n<p><h4>\u4f7f\u7528Matplotlib\u7ed8\u5236\u51b3\u7b56\u8fb9\u754c<\/h4>\n<\/p>\n<p><p>\u6700\u540e\uff0c\u6211\u4eec\u5c06\u4f7f\u7528Matplotlib\u7684<code>contourf<\/code>\u51fd\u6570\u6839\u636e\u6a21\u578b\u7684\u9884\u6d4b\u7ed3\u679c\u753b\u51fa\u51b3\u7b56\u8fb9\u754c\u3002\u901a\u8fc7\u4e0d\u540c\u989c\u8272\u7684\u533a\u57df\u6765\u8868\u793a\u4e0d\u540c\u7684\u5206\u7c7b\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u8c03\u6574\u4e0e\u4f18\u5316<\/h3>\n<\/p>\n<p><p>\u5728\u7ed8\u5236\u4e86\u521d\u6b65\u7684\u51b3\u7b56\u8fb9\u754c\u4ee5\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u4e00\u6b65\u5bf9\u6a21\u578b\u8fdb\u884c\u8c03\u6574\uff0c\u4f18\u5316\u51b3\u7b56\u8fb9\u754c\u7684\u5f62\u72b6\u3002<\/p>\n<\/p>\n<p><h4>\u8c03\u6574\u6a21\u578b\u53c2\u6570<\/h4>\n<\/p>\n<p><p>\u5bf9\u6a21\u578b\u7684\u5404\u79cd\u53c2\u6570\u8fdb\u884c\u8c03\u6574\uff0c\u6bd4\u5982SVM\u4e2d\u7684C\u3001gamma\u503c\u3001\u6216\u8005\u51b3\u7b56\u6811\u7684\u6700\u5927\u6df1\u5ea6\uff0c\u4ee5\u671f\u83b7\u5f97\u66f4\u597d\u7684\u5206\u7c7b\u6548\u679c\u3002<\/p>\n<\/p>\n<p><h4>\u8de8\u9a8c\u8bc1\u4e0e\u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n<p><p>\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u6280\u672f\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\u548c\u9009\u62e9\uff0c\u786e\u4fdd\u6211\u4eec\u7684\u6a21\u578b\u4e0d\u4ec5\u5728\u8bad\u7ec3\u6570\u636e\u4e0a\u8868\u73b0\u5f97\u597d\uff0c\u540c\u65f6\u4e5f\u80fd\u5728\u672a\u77e5\u6570\u636e\u4e0a\u4fdd\u6301\u826f\u597d\u7684\u9884\u6d4b\u80fd\u529b\u3002<\/p>\n<\/p>\n<p><h2>\u603b\u7ed3<\/h2>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u5c31\u80fd\u591f\u5728Python\u4e2d\u4f7f\u7528Scikit-learn\u548cMatplotlib\u6765\u7ed8\u5236\u51fa\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\u4e86\u3002\u901a\u8fc7\u8fd9\u6837\u7684\u53ef\u89c6\u5316\uff0c\u80fd\u591f\u8ba9\u6211\u4eec\u5bf9\u6a21\u578b\u7684\u5206\u7c7b\u6548\u679c\u6709\u4e00\u4e2a\u76f4\u89c2\u7684\u4e86\u89e3\uff0c\u5e76\u8f85\u52a9\u6211\u4eec\u8fdb\u884c\u6a21\u578b\u7684\u9009\u62e9\u548c\u53c2\u6570\u7684\u8c03\u6574\u3002\u8fd9\u5bf9\u4e8e\u5904\u7406\u5b9e\u9645\u7684\u673a\u5668\u5b66\u4e60\u95ee\u9898\u662f\u5341\u5206\u6709\u4ef7\u503c\u7684\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p><strong>Q1\uff1aPython\u4e2d\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u7ed8\u5236\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\u7684\u65b9\u6cd5\uff1f<\/strong><br \/>\n\u5728Python\u4e2d\uff0c\u6709\u51e0\u79cd\u5e38\u7528\u7684\u65b9\u6cd5\u6765\u7ed8\u5236\u975e\u7ebf\u6027\u51b3\u7b56\u8fb9\u754c\u3002\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u6838\u51fd\u6570\uff08kernel function\uff09\u6765\u5bf9\u7279\u5f81\u8fdb\u884c\u975e\u7ebf\u6027\u6620\u5c04\uff0c\u7136\u540e\u4f7f\u7528\u652f\u6301\u5411\u91cf\u673a\uff08Support Vector Machine\uff0cSVM\uff09\u6765\u8fdb\u884c\u5206\u7c7b\u3002\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\uff08Artificial Neural Network\uff0cANN\uff09\u6765\u5efa\u7acb\u591a\u5c42\u7ea7\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u4ece\u800c\u5b9e\u73b0\u975e\u7ebf\u6027\u5206\u7c7b\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528\u51b3\u7b56\u6811\u7b97\u6cd5\uff0c\u5982\u968f\u673a\u68ee\u6797\uff08Random Forest\uff09\u548c\u68af\u5ea6\u63d0\u5347\u51b3\u7b56\u6811\uff08Gradient Boosting Decision 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