{"id":189403,"date":"2024-05-09T17:35:34","date_gmt":"2024-05-09T09:35:34","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/189403.html"},"modified":"2024-05-09T17:35:38","modified_gmt":"2024-05-09T09:35:38","slug":"%e6%9c%89%e6%b2%a1%e6%9c%89%e5%93%aa%e9%87%8c%e6%8f%90%e4%be%9b%e4%ba%86%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e6%95%b0%e6%8d%ae%e6%8c%96%e6%8e%98%e7%ae%97%e6%b3%95%e7%9a%84%e5%9f%ba%e6%9c%ac%e5%ae%9e","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/189403.html","title":{"rendered":"\u6709\u6ca1\u6709\u54ea\u91cc\u63d0\u4f9b\u4e86\u673a\u5668\u5b66\u4e60\u6570\u636e\u6316\u6398\u7b97\u6cd5\u7684\u57fa\u672c\u5b9e\u73b0"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/26095421\/a4ecdfd5-9d22-47b5-913c-ea3cc330652a.webp\" alt=\"\u6709\u6ca1\u6709\u54ea\u91cc\u63d0\u4f9b\u4e86\u673a\u5668\u5b66\u4e60\u6570\u636e\u6316\u6398\u7b97\u6cd5\u7684\u57fa\u672c\u5b9e\u73b0\" \/><\/p>\n<p><p><strong><a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u6570\u636e\u6316\u6398\u7b97\u6cd5\u7684\u57fa\u672c\u5b9e\u73b0\u4e3b\u8981\u53ef\u4ee5\u5728\u5f00\u6e90\u5e93\u3001\u5728\u7ebf\u5e73\u53f0\u3001\u6559\u80b2\u8d44\u6e90\u4ee5\u53ca\u4e13\u4e1a\u8f6f\u4ef6\u4e2d\u627e\u5230<\/strong>\u3002\u5176\u4e2d\uff0c\u5f00\u6e90\u5de5\u5177\u63d0\u4f9b\u4e86\u6700\u4e3a\u5e7f\u6cdb\u548c\u6df1\u5165\u7684\u8d44\u6e90\u3002\u4f8b\u5982\uff0cPython\u7684scikit-learn\u5e93\u5e7f\u6cdb\u5e94\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u5305\u62ec\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u7b49\u591a\u79cd\u7b97\u6cd5\uff1bR\u8bed\u8a00\u4e5f\u88ab\u5e7f\u6cdb\u7528\u4e8e\u7edf\u8ba1\u5206\u6790\u548c\u6570\u636e\u6316\u6398\uff0c\u5e76\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u5305\u5b9e\u73b0\u8fd9\u4e9b\u7b97\u6cd5\uff1b\u5728\u7ebf\u5e73\u53f0\u5982Kaggle\u63d0\u4f9b\u4e86\u5b9e\u8df5\u673a\u4f1a\u548c\u5f00\u6e90\u4ee3\u7801\uff1b\u6559\u80b2\u8d44\u6e90\u6bd4\u5982\u5728\u7ebf\u8bfe\u7a0b\u3001\u4e66\u7c4d\u5e38\u5e38\u9644\u5e26\u5177\u4f53\u5b9e\u4f8b\u548c\u4ee3\u7801\uff1b\u800c\u4e13\u4e1a\u8f6f\u4ef6\uff0c\u5982SAS\u3001IBM SPSS\u7b49\uff0c\u867d\u7136\u662f\u5546\u4e1a\u4ea7\u54c1\uff0c\u4f46\u5b83\u4eec\u901a\u5e38\u6709\u8bd5\u7528\u7248\u6216\u5b66\u672f\u7248\uff0c\u4e5f\u63d0\u4f9b\u5b9e\u73b0\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u529f\u80fd\u3002\u4e0b\u9762\u6211\u4f1a\u5177\u4f53\u4ecb\u7ecd\u51e0\u4e2a\u8fd9\u6837\u7684\u8d44\u6e90\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u5f00\u6e90\u5e93<\/h3>\n<\/p>\n<p><p><strong>\u5f00\u6e90\u5e93\u5728\u673a\u5668\u5b66\u4e60\u53ca\u6570\u636e\u6316\u6398\u9886\u57df\u5177\u6709\u6781\u5176\u91cd\u8981\u7684\u4f5c\u7528<\/strong>\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7b97\u6cd5\u57fa\u672c\u5b9e\u73b0\uff0c\u4e14\u591a\u6570\u90fd\u662f\u514d\u8d39\u53ef\u7528\u3002<\/p>\n<\/p>\n<p><h4>scikit-learn<\/h4>\n<\/p>\n<p><p>scikit-learn\u662fPython\u7f16\u7a0b\u8bed\u8a00\u7684\u4e00\u4e2a\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u5e93\u3002\u5b83\u652f\u6301\u591a\u79cd\u76d1\u7763\u548c\u975e\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\u3002\u5728scikit-learn\u4e2d\uff0c\u60a8\u53ef\u4ee5\u627e\u5230\u8bb8\u591a\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5982\u968f\u673a\u68ee\u6797\u3001\u652f\u6301\u5411\u91cf\u673a\u3001k-\u5747\u503c\u805a\u7c7b\u7b49\u3002\u8be5\u5e93\u6ce8\u91cd\u6613\u7528\u6027\u548c\u7075\u6d3b\u6027\uff0c\u662f\u521d\u5b66\u8005\u548c\u4e13\u4e1a\u6570\u636e\u79d1\u5b66\u5bb6\u5e38\u7528\u7684\u5de5\u5177\u4e4b\u4e00\u3002<\/p>\n<\/p>\n<p><p>\u4ee3\u7801\u793a\u4f8b\uff1a<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">from sklearn.ensemble import RandomForestClassifier<\/p>\n<p>clf = RandomForestClassifier(n_estimators=100)<\/p>\n<p>clf.fit(X_tr<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>n, y_train)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>TensorFlow<\/h4>\n<\/p>\n<p><p>TensorFlow\u662fGoogle\u5f00\u6e90\u7684\u4e00\u4e2a\u7528\u6765\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u7814\u7a76\u7684\u8ba1\u7b97\u6846\u67b6\u3002\u5b83\u4e0d\u4ec5\u652f\u6301\u57fa\u672c\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u8fd8\u80fd\u8fd0\u7528\u4e8e\u590d\u6742\u7684\u795e\u7ecf\u7f51\u7edc\u8bbe\u8ba1\u3002\u5b83\u5f3a\u5927\u7684\u8ba1\u7b97\u80fd\u529b\u4f7f\u5176\u5728\u89c6\u89c9\u8bc6\u522b\u3001\u8bed\u97f3\u8bc6\u522b\u7b49\u9886\u57df\u5c24\u4e3a\u7a81\u51fa\u3002<\/p>\n<\/p>\n<p><h4>PyTorch<\/h4>\n<\/p>\n<p><p>PyTorch\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u7528\u4e8e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49\u5e94\u7528\u7a0b\u5e8f\u3002\u5b83\u4e0eTensorFlow\u5e76\u9a7e\u9f50\u9a71\uff0c\u88ab\u8ba4\u4e3a\u66f4\u6613\u4e8e\u5b9e\u9a8c\uff0c\u7531\u4e8e\u5176\u52a8\u6001\u8ba1\u7b97\u56fe\u7279\u6027\uff0c\u6df1\u53d7\u7814\u7a76\u4eba\u5458\u559c\u7231\u3002<\/p>\n<\/p>\n<p><h3>\u4e8c\u3001\u5728\u7ebf\u5e73\u53f0<\/h3>\n<\/p>\n<p><p><strong>\u5728\u7ebf\u5e73\u53f0\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u6570\u636e\u96c6\u3001\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u6bd4\u8d5b\u548c\u4ea4\u6d41\u73af\u5883<\/strong>\uff0c\u5e2e\u52a9\u4eba\u4eec\u5b66\u4e60\u3001\u4f7f\u7528\u548c\u6539\u8fdb\u8fd9\u4e9b\u7b97\u6cd5\u3002<\/p>\n<\/p>\n<p><h4>Kaggle<\/h4>\n<\/p>\n<p><p>Kaggle\u662f\u4e00\u4e2a\u4e3a\u5f00\u53d1\u5546\u548c\u6570\u636e\u79d1\u5b66\u5bb6\u63d0\u4f9b\u673a\u5668\u5b66\u4e60\u7ade\u8d5b\u7684\u5e73\u53f0\u3002\u4e0d\u4ec5\u53ef\u4ee5\u5728Kaggle\u4e0a\u627e\u5230\u4f17\u591a\u7684\u7b97\u6cd5\u5b9e\u73b0\u793a\u4f8b\uff0c\u8fd8\u53ef\u4ee5\u8bbf\u95ee\u5927\u91cf\u7684\u516c\u5f00\u6570\u636e\u96c6\uff0c\u5e76\u53ef\u4ee5\u4e0e\u6765\u81ea\u5168\u7403\u7684\u6570\u636e\u79d1\u5b66\u5bb6\u4ea4\u6d41\u3002<\/p>\n<\/p>\n<p><h4>Google Colab<\/h4>\n<\/p>\n<p><p>Google Colab\u662f\u4e00\u4e2a\u514d\u8d39\u7684\u4e91\u670d\u52a1\uff0c\u5e76\u652f\u6301\u514d\u8d39\u7684GPU\u3002\u4f60\u53ef\u4ee5\u76f4\u63a5\u5728\u6d4f\u89c8\u5668\u4e2d\u5199Python\u4ee3\u7801\uff0c\u8fd9\u4e9b\u4ee3\u7801\u53ef\u4ee5\u901a\u8fc7Google\u7684\u4e91\u670d\u52a1\u5668\u6765\u6267\u884c\u3002<\/p>\n<\/p>\n<p><h3>\u4e09\u3001\u6559\u80b2\u8d44\u6e90<\/h3>\n<\/p>\n<p><p><strong>\u6559\u80b2\u8d44\u6e90\u63d0\u4f9b\u4e86\u5b66\u4e60\u548c\u5b9e\u8df5\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u673a\u4f1a\uff0c\u5e76\u4e14\u5f80\u5f80\u5305\u542b\u5927\u91cf\u7684\u5b9e\u4f8b\u548c\u9879\u76ee<\/strong>\u3002<\/p>\n<\/p>\n<p><h4>\u673a\u5668\u5b66\u4e60\u8bfe\u7a0b<\/h4>\n<\/p>\n<p><p>\u5982Coursera\u3001edX\u548cUdacity\u7b49\u5728\u7ebf\u6559\u80b2\u5e73\u53f0\u63d0\u4f9b\u4e86\u6765\u81ea\u4e16\u754c\u9876\u7ea7\u5927\u5b66\u7684\u673a\u5668\u5b66\u4e60\u8bfe\u7a0b\u3002\u8fd9\u4e9b\u8bfe\u7a0b\u6db5\u76d6\u4e86\u7406\u8bba\u53ca\u5b9e\u8df5\u4e24\u65b9\u9762\uff0c\u5e76\u901a\u5e38\u914d\u6709\u6559\u7a0b\u548c\u8bfe\u540e\u9879\u76ee\u3002<\/p>\n<\/p>\n<p><h4>\u4e66\u7c4d<\/h4>\n<\/p>\n<p><p>\u5e02\u9762\u4e0a\u4e5f\u6709\u4e0d\u5c11\u4f18\u79c0\u7684\u673a\u5668\u5b66\u4e60\u4e66\u7c4d\uff0c\u5982\u300aPython\u673a\u5668\u5b66\u4e60\u300b\u3001\u300a\u6df1\u5165\u6d45\u51fa\u673a\u5668\u5b66\u4e60\u300b\u7b49\uff0c\u8fd9\u4e9b\u4e66\u7c4d\u4e0d\u4ec5\u8bb2\u8ff0\u673a\u5668\u5b66\u4e60\u7684\u57fa\u672c\u539f\u7406\uff0c\u800c\u4e14\u63d0\u4f9b\u4e86\u5b9e\u9645\u4ee3\u7801\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u4e13\u4e1a\u8f6f\u4ef6<\/h3>\n<\/p>\n<p><p><strong>\u7279\u5b9a\u7684\u4e13\u4e1a\u8f6f\u4ef6\u53ef\u80fd\u63d0\u4f9b\u4e86\u66f4\u6613\u64cd\u4f5c\u3001\u6216\u8005\u9488\u5bf9\u7279\u5b9a\u884c\u4e1a\u4f18\u5316\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u5b9e\u73b0<\/strong>\u3002<\/p>\n<\/p>\n<p><h4>SAS<\/h4>\n<\/p>\n<p><p>SAS\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7edf\u8ba1\u8f6f\u4ef6\u5305\uff0c\u63d0\u4f9b\u4e86\u5e7f\u6cdb\u7684\u7edf\u8ba1\u529f\u80fd\uff0c\u5305\u62ec\u5bf9\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u652f\u6301\uff0c\u5c24\u5176\u662f\u5728\u5546\u4e1a\u9886\u57df\u7684\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n<p><h4>IBM SPSS<\/h4>\n<\/p>\n<p><p>SPSS\u662fIBM\u63a8\u51fa\u7684\u4e00\u6b3e\u529f\u80fd\u5f3a\u5927\u7684\u7edf\u8ba1\u5206\u6790\u5de5\u5177\uff0c\u4e5f\u652f\u6301\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5c24\u5176\u5728\u793e\u4f1a\u79d1\u5b66\u7814\u7a76\u4e2d\u5f97\u5230\u4e86\u5e7f\u6cdb\u5e94\u7528\u3002<\/p>\n<\/p>\n<p><p>\u603b\u4e4b\uff0c\u60f3\u8981\u627e\u5230\u673a\u5668\u5b66\u4e60\u6570\u636e\u6316\u6398\u7b97\u6cd5\u7684\u57fa\u672c\u5b9e\u73b0\uff0c\u53ef\u4ee5\u4ece\u4e0a\u8ff0\u51e0\u4e2a\u4e3b\u8981\u6e20\u9053\u7740\u624b\u3002\u65e0\u8bba\u662f\u901a\u8fc7\u5f00\u6e90\u5e93\u3001\u5728\u7ebf\u5e73\u53f0\u3001\u6559\u80b2\u8d44\u6e90\uff0c\u8fd8\u662f\u4e13\u4e1a\u8f6f\u4ef6\uff0c\u7528\u6237\u90fd\u80fd\u591f\u83b7\u5f97\u76f8\u5e94\u7684\u6559\u7a0b\u548c\u652f\u6301\u8fdb\u800c\u638c\u63e1\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u6838\u5fc3\u6280\u672f\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<ol>\n<li>\n<p><strong>\u4f60\u80fd\u63a8\u8350\u4e00\u4e9b\u63d0\u4f9b\u57fa\u672c\u673a\u5668\u5b66\u4e60\u6570\u636e\u6316\u6398\u7b97\u6cd5\u5b9e\u73b0\u7684\u8d44\u6e90\u5417\uff1f<\/strong><br \/>\n\u5f53\u4eca\u5e02\u573a\u4e0a\u6709\u8bb8\u591a\u8d44\u6e90\u63d0\u4f9b\u673a\u5668\u5b66\u4e60\u6570\u636e\u6316\u6398\u7b97\u6cd5\u7684\u57fa\u672c\u5b9e\u73b0\u3002\u4f60\u53ef\u4ee5\u5c1d\u8bd5\u67e5\u627e\u4e00\u4e9b\u77e5\u540d\u7684\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u6bd4\u5982Scikit-learn\u3001TensorFlow\u548cPyTorch\u7b49\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u6570\u636e\u6316\u6398\u5de5\u5177\u7684\u5b9e\u73b0\uff0c\u4f60\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u9009\u62e9\u76f8\u5e94\u7684\u7b97\u6cd5\u8fdb\u884c\u5b9e\u8df5\u548c\u63a2\u7d22\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6709\u6ca1\u6709\u9002\u5408\u521d\u5b66\u8005\u7684\u673a\u5668\u5b66\u4e60\u6570\u636e\u6316\u6398\u7b97\u6cd5\u5b9e\u73b0\u6559\u7a0b\uff1f<\/strong><br 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