{"id":740,"date":"2025-04-10T12:00:03","date_gmt":"2025-04-10T04:00:03","guid":{"rendered":"https:\/\/www.wunen.com\/index.php\/2025\/04\/10\/%e6%95%b0%e5%ad%97%e5%8c%96%e4%bd%93%e8%82%b2%ef%bc%9a%e8%bf%90%e5%8a%a8%e5%91%98%e7%9a%84%e8%ae%ad%e7%bb%83%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90\/"},"modified":"2025-04-10T12:00:03","modified_gmt":"2025-04-10T04:00:03","slug":"%e6%95%b0%e5%ad%97%e5%8c%96%e4%bd%93%e8%82%b2%ef%bc%9a%e8%bf%90%e5%8a%a8%e5%91%98%e7%9a%84%e8%ae%ad%e7%bb%83%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"http:\/\/www.wunen.com\/index.php\/2025\/04\/10\/%e6%95%b0%e5%ad%97%e5%8c%96%e4%bd%93%e8%82%b2%ef%bc%9a%e8%bf%90%e5%8a%a8%e5%91%98%e7%9a%84%e8%ae%ad%e7%bb%83%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90\/","title":{"rendered":"\u6570\u5b57\u5316\u4f53\u80b2\uff1a\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790"},"content":{"rendered":"<div class=\"article_content clearfix\" id=\"article_content\">\n <link href=\"https:\/\/csdnimg.cn\/release\/blogv2\/dist\/mdeditor\/css\/editerView\/kdoc_html_views-1a98987dfd.css\" rel=\"stylesheet\"\/>\n <link href=\"https:\/\/csdnimg.cn\/release\/blogv2\/dist\/mdeditor\/css\/editerView\/ck_htmledit_views-704d5b9767.css\" rel=\"stylesheet\"\/>\n<div class=\"htmledit_views\" id=\"content_views\">\n<div style=\"font-size: 16px;\">\n<h2>\n    1.\u80cc\u666f\u4ecb\u7ecd<br \/>\n   <\/h2>\n<p>\n    \u968f\u7740\u79d1\u6280\u7684\u53d1\u5c55\uff0c\u4f53\u80b2\u8fd0\u52a8\u7684\u6570\u5b57\u5316\u5df2\u7ecf\u6210\u4e3a\u73b0\u4ee3\u4f53\u80b2\u7684\u4e0d\u53ef\u6216\u7f3a\u7684\u4e00\u90e8\u5206\u3002\u6570\u5b57\u5316\u4f53\u80b2\u6d89\u53ca\u5230\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\uff0c\u4e3a\u8fd0\u52a8\u5458\u63d0\u4f9b\u66f4\u6709\u6548\u7684\u8bad\u7ec3\u65b9\u6cd5\u548c\u7ade\u6280\u4f18\u52bf\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8\u6570\u5b57\u5316\u4f53\u80b2\u7684\u80cc\u666f\u3001\u6838\u5fc3\u6982\u5ff5\u3001\u7b97\u6cd5\u539f\u7406\u3001\u4ee3\u7801\u5b9e\u4f8b\u4ee5\u53ca\u672a\u6765\u53d1\u5c55\u8d8b\u52bf\u3002\n   <\/p>\n<h3>\n    1.1 \u80cc\u666f\u4ecb\u7ecd<br \/>\n   <\/h3>\n<p>\n    \u6570\u5b57\u5316\u4f53\u80b2\u662f\u6307\u5229\u7528\u6570\u5b57\u6280\u672f\u3001\u4e92\u8054\u7f51\u548c\u5927\u6570\u636e\u5206\u6790\u7b49\u65b9\u6cd5\uff0c\u5bf9\u4f53\u80b2\u8fd0\u52a8\u8fdb\u884c\u5168\u9762\u7684\u6570\u5b57\u5316\u5904\u7406\u3002\u8fd9\u79cd\u6280\u672f\u5728\u4f53\u80b2\u8fd0\u52a8\u4e2d\u7684\u5e94\u7528\u5df2\u7ecf\u53d6\u5f97\u4e86\u663e\u8457\u7684\u6210\u679c\uff0c\u5305\u62ec\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\u3001\u6bd4\u8d5b\u7ade\u6280\u5206\u6790\u3001\u8fd0\u52a8\u5458\u5065\u5eb7\u76d1\u6d4b\u7b49\u3002\n   <\/p>\n<p>\n    \u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\u662f\u6570\u5b57\u5316\u4f53\u80b2\u7684\u4e00\u4e2a\u91cd\u8981\u73af\u8282\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u8fd0\u52a8\u5458\u66f4\u6709\u6548\u5730\u8fdb\u884c\u8bad\u7ec3\uff0c\u63d0\u9ad8\u7ade\u6280\u80fd\u529b\uff0c\u964d\u4f4e\u4f24\u5bb3\u98ce\u9669\u3002\u901a\u8fc7\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u83b7\u53d6\u8fd0\u52a8\u5458\u5728\u8bad\u7ec3\u4e2d\u7684\u5404\u79cd\u6307\u6807\u6570\u636e\uff0c\u5982\u5fc3\u7387\u3001\u8fd0\u52a8\u91cf\u3001\u75b2\u52b3\u7a0b\u5ea6\u7b49\uff0c\u4ece\u800c\u4e3a\u8fd0\u52a8\u5458\u63d0\u4f9b\u4e2a\u6027\u5316\u7684\u8bad\u7ec3\u5efa\u8bae\u3002\n   <\/p>\n<h3>\n    1.2 \u6838\u5fc3\u6982\u5ff5\u4e0e\u8054\u7cfb<br \/>\n   <\/h3>\n<p>\n    \u5728\u8fdb\u884c\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u4e86\u89e3\u4e00\u4e9b\u6838\u5fc3\u6982\u5ff5\u548c\u8054\u7cfb\uff1a\n   <\/p>\n<ul>\n<li>\n     <strong><br \/>\n      \u8bad\u7ec3\u6570\u636e<br \/>\n     <\/strong><br \/>\n     \uff1a\u8fd0\u52a8\u5458\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4ea7\u751f\u7684\u5404\u79cd\u6307\u6807\u6570\u636e\uff0c\u5982\u5fc3\u7387\u3001\u8fd0\u52a8\u91cf\u3001\u75b2\u52b3\u7a0b\u5ea6\u7b49\u3002\n    <\/li>\n<li>\n     <strong><br \/>\n      \u6570\u636e\u5206\u6790<br \/>\n     <\/strong><br \/>\n     \uff1a\u901a\u8fc7\u5bf9\u8bad\u7ec3\u6570\u636e\u7684\u7edf\u8ba1\u3001\u6a21\u578b\u6784\u5efa\u548c\u9884\u6d4b\u7b49\u65b9\u6cd5\uff0c\u4e3a\u8fd0\u52a8\u5458\u63d0\u4f9b\u4e2a\u6027\u5316\u7684\u8bad\u7ec3\u5efa\u8bae\u3002\n    <\/li>\n<li>\n     <strong><br \/>\n      \u4e2a\u6027\u5316\u8bad\u7ec3<br \/>\n     <\/strong><br \/>\n     \uff1a\u6839\u636e\u8fd0\u52a8\u5458\u7684\u7279\u70b9\u548c\u9700\u6c42\uff0c\u4e3a\u5176\u5236\u5b9a\u7684\u8bad\u7ec3\u8ba1\u5212\u3002\n    <\/li>\n<li>\n     <strong><br \/>\n      \u7ade\u6280\u4f18\u52bf<br \/>\n     <\/strong><br \/>\n     \uff1a\u8fd0\u52a8\u5458\u5728\u7ade\u6280\u4e2d\u7684\u4f18\u52bf\uff0c\u5982\u901f\u5ea6\u3001\u529b\u91cf\u3001\u7075\u6d3b\u6027\u7b49\u3002\n    <\/li>\n<\/ul>\n<h3>\n    1.3 \u6838\u5fc3\u7b97\u6cd5\u539f\u7406\u548c\u5177\u4f53\u64cd\u4f5c\u6b65\u9aa4\u4ee5\u53ca\u6570\u5b66\u6a21\u578b\u516c\u5f0f\u8be6\u7ec6\u8bb2\u89e3<br \/>\n   <\/h3>\n<p>\n    \u5728\u8fdb\u884c\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u7b97\u6cd5\u539f\u7406\u548c\u6570\u5b66\u6a21\u578b\uff1a\n   <\/p>\n<h4>\n    1.3.1 \u7ebf\u6027\u56de\u5f52\u6a21\u578b<br \/>\n   <\/h4>\n<p>\n    \u7ebf\u6027\u56de\u5f52\u6a21\u578b\u662f\u4e00\u79cd\u5e38\u7528\u7684\u9884\u6d4b\u6a21\u578b\uff0c\u7528\u4e8e\u9884\u6d4b\u4e00\u4e2a\u53d8\u91cf\u7684\u503c\uff0c\u6839\u636e\u5176\u4ed6\u53d8\u91cf\u7684\u503c\u3002\u5728\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u8fd0\u52a8\u5458\u5728\u4e0d\u540c\u8bad\u7ec3\u6761\u4ef6\u4e0b\u7684\u7ade\u6280\u8868\u73b0\u3002\n   <\/p>\n<p>\n    \u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u6570\u5b66\u6a21\u578b\u516c\u5f0f\u4e3a\uff1a\n   <\/p>\n<p>\n    $$ y = \\beta<br \/>\n    <em><br \/>\n     0 + \\beta<br \/>\n    <\/em><br \/>\n    1x<br \/>\n    <em><br \/>\n     1 + \\beta<br \/>\n    <\/em><br \/>\n    2x<br \/>\n    <em><br \/>\n     2 + &#8230; + \\beta<br \/>\n    <\/em><br \/>\n    nx_n + \\epsilon $$\n   <\/p>\n<p>\n    \u5176\u4e2d\uff0c$y$ \u662f\u9884\u6d4b\u53d8\u91cf(\u7ade\u6280\u8868\u73b0)\uff0c$x<br \/>\n    <em><br \/>\n     1, x<br \/>\n    <\/em><br \/>\n    2, &#8230;, x<br \/>\n    <em><br \/>\n     n$ \u662f\u9884\u6d4b\u56e0\u5b50(\u8bad\u7ec3\u6307\u6807)\uff0c$\\beta<br \/>\n    <\/em><br \/>\n    0, \\beta<br \/>\n    <em><br \/>\n     1, &#8230;, \\beta<br \/>\n    <\/em><br \/>\n    n$ \u662f\u53c2\u6570\uff0c$\\epsilon$ \u662f\u8bef\u5dee\u9879\u3002\n   <\/p>\n<h4>\n    1.3.2 \u652f\u6301\u5411\u91cf\u673a(SVM)<br \/>\n   <\/h4>\n<p>\n    \u652f\u6301\u5411\u91cf\u673a\u662f\u4e00\u79cd\u591a\u7c7b\u522b\u5206\u7c7b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u95ee\u9898\u3002\u5728\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u652f\u6301\u5411\u91cf\u673a\u6765\u5206\u7c7b\u8fd0\u52a8\u5458\u7684\u7ade\u6280\u8868\u73b0\uff0c\u4ece\u800c\u4e3a\u8fd0\u52a8\u5458\u63d0\u4f9b\u4e2a\u6027\u5316\u7684\u8bad\u7ec3\u5efa\u8bae\u3002\n   <\/p>\n<p>\n    \u652f\u6301\u5411\u91cf\u673a\u7684\u6570\u5b66\u6a21\u578b\u516c\u5f0f\u4e3a\uff1a\n   <\/p>\n<p>\n    $$ \\min<br \/>\n    <em><br \/>\n     {\\mathbf{w},b} \\frac{1}{2}\\mathbf{w}^T\\mathbf{w} + C\\sum<br \/>\n    <\/em><br \/>\n    {i=1}^n\\xi_i $$\n   <\/p>\n<p>\n    $$ y<br \/>\n    <em><br \/>\n     i(\\mathbf{w}^T\\mathbf{x}<br \/>\n    <\/em><br \/>\n    i + b) \\geq 1 &#8211; \\xi<br \/>\n    <em><br \/>\n     i, \\xi<br \/>\n    <\/em><br \/>\n    i \\geq 0 $$\n   <\/p>\n<p>\n    \u5176\u4e2d\uff0c$\\mathbf{w}$ \u662f\u652f\u6301\u5411\u91cf\u673a\u7684\u6743\u91cd\u5411\u91cf\uff0c$b$ \u662f\u504f\u7f6e\u9879\uff0c$C$ \u662f\u6b63\u5219\u5316\u53c2\u6570\uff0c$\\xi<br \/>\n    <em><br \/>\n     i$ \u662f\u677e\u5f1b\u53d8\u91cf\uff0c$y<br \/>\n    <\/em><br \/>\n    i$ \u662f\u6837\u672c\u7684\u6807\u7b7e\uff0c$\\mathbf{x}_i$ \u662f\u6837\u672c\u7684\u7279\u5f81\u5411\u91cf\u3002\n   <\/p>\n<h4>\n    1.3.3 \u968f\u673a\u68ee\u6797<br \/>\n   <\/h4>\n<p>\n    \u968f\u673a\u68ee\u6797\u662f\u4e00\u79cd\u96c6\u6210\u5b66\u4e60\u65b9\u6cd5\uff0c\u901a\u8fc7\u6784\u5efa\u591a\u4e2a\u51b3\u7b56\u6811\uff0c\u5e76\u5c06\u5b83\u4eec\u7684\u9884\u6d4b\u7ed3\u679c\u8fdb\u884c\u5e73\u5747\uff0c\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002\u5728\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u968f\u673a\u68ee\u6797\u6765\u9884\u6d4b\u8fd0\u52a8\u5458\u5728\u4e0d\u540c\u8bad\u7ec3\u6761\u4ef6\u4e0b\u7684\u7ade\u6280\u8868\u73b0\u3002\n   <\/p>\n<p>\n    \u968f\u673a\u68ee\u6797\u7684\u6570\u5b66\u6a21\u578b\u516c\u5f0f\u4e3a\uff1a\n   <\/p>\n<p>\n    $$ \\hat{y} = \\frac{1}{K}\\sum<br \/>\n    <em><br \/>\n     {k=1}^K f<br \/>\n    <\/em><br \/>\n    k(\\mathbf{x}) $$\n   <\/p>\n<p>\n    \u5176\u4e2d\uff0c$\\hat{y}$ \u662f\u9884\u6d4b\u53d8\u91cf(\u7ade\u6280\u8868\u73b0)\uff0c$K$ \u662f\u51b3\u7b56\u6811\u7684\u6570\u91cf\uff0c$f_k(\\mathbf{x})$ \u662f\u7b2c$k$\u4e2a\u51b3\u7b56\u6811\u7684\u9884\u6d4b\u503c\u3002\n   <\/p>\n<h4>\n    1.3.4 \u65f6\u95f4\u5e8f\u5217\u5206\u6790<br \/>\n   <\/h4>\n<p>\n    \u65f6\u95f4\u5e8f\u5217\u5206\u6790\u662f\u4e00\u79cd\u7528\u4e8e\u5206\u6790\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7528\u4e8e\u5206\u6790\u8fd0\u52a8\u5458\u5728\u4e0d\u540c\u65f6\u95f4\u70b9\u7684\u8bad\u7ec3\u6307\u6807\u6570\u636e\u3002\u5728\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u6570\u636e\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u6765\u5206\u6790\u8fd0\u52a8\u5458\u7684\u8bad\u7ec3\u8d8b\u52bf\uff0c\u4ece\u800c\u4e3a\u8fd0\u52a8\u5458\u63d0\u4f9b\u4e2a\u6027\u5316\u7684\u8bad\u7ec3\u5efa\u8bae\u3002\n   <\/p>\n<p>\n    \u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7684\u6570\u5b66\u6a21\u578b\u516c\u5f0f\u4e3a\uff1a\n   <\/p>\n<p>\n    $$ y<br \/>\n    <em><br \/>\n     t = \\phi y<br \/>\n    <\/em><br \/>\n    {t-1} + \\epsilon_t $$\n   <\/p>\n<p>\n    \u5176\u4e2d\uff0c$y<br \/>\n    <em><br \/>\n     t$ \u662f\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u503c\u5728\u65f6\u95f4\u70b9$t$\uff0c$\\phi$ \u662f\u53c2\u6570\uff0c$\\epsilon<br \/>\n    <\/em><br \/>\n    t$ \u662f\u8bef\u5dee\u9879\u3002\n   <\/p>\n<h3>\n    1.4 \u5177\u4f53\u4ee3\u7801\u5b9e\u4f8b\u548c\u8be6\u7ec6\u89e3\u91ca\u8bf4\u660e<br \/>\n   <\/h3>\n<p>\n    \u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u7ed9\u51fa\u4e00\u4e2a\u5177\u4f53\u7684\u4ee3\u7801\u5b9e\u4f8b\uff0c\u4ee5\u53ca\u5176\u8be6\u7ec6\u89e3\u91ca\u8bf4\u660e\u3002\n   <\/p>\n<h4>\n    1.4.1 \u7ebf\u6027\u56de\u5f52\u6a21\u578b<br \/>\n   <\/h4>\n<p>\n    &#8220;`python import numpy as np import pandas as pd from sklearn.linear<br \/>\n    <em><br \/>\n     model import LinearRegression from sklearn.model<br \/>\n    <\/em><br \/>\n    selection import train<br \/>\n    <em><br \/>\n     test<br \/>\n    <\/em><br \/>\n    split from sklearn.metrics import mean<br \/>\n    <em><br \/>\n     squared<br \/>\n    <\/em><br \/>\n    error\n   <\/p>\n<h2>\n    \u52a0\u8f7d\u6570\u636e<br \/>\n   <\/h2>\n<p>\n    data = pd.read<br \/>\n    <em><br \/>\n     csv(&#8216;training<br \/>\n    <\/em><br \/>\n    data.csv&#8217;)\n   <\/p>\n<h2>\n    \u9009\u53d6\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf<br \/>\n   <\/h2>\n<p>\n    X = data[[&#8216;heart<br \/>\n    <em><br \/>\n     rate&#8217;, &#8216;training<br \/>\n    <\/em><br \/>\n    load&#8217;, &#8216;fatigue<br \/>\n    <em><br \/>\n     level&#8217;]] y = data[&#8216;competition<br \/>\n    <\/em><br \/>\n    performance&#8217;]\n   <\/p>\n<h2>\n    \u8bad\u7ec3-\u6d4b\u8bd5\u6570\u636e\u96c6\u5206\u5272<br \/>\n   <\/h2>\n<p>\n    X<br \/>\n    <em><br \/>\n     train, X<br \/>\n    <\/em><br \/>\n    test, y<br \/>\n    <em><br \/>\n     train, y<br \/>\n    <\/em><br \/>\n    test = train<br \/>\n    <em><br \/>\n     test<br \/>\n    <\/em><br \/>\n    split(X, y, test<br \/>\n    <em><br \/>\n     size=0.2, random<br \/>\n    <\/em><br \/>\n    state=42)\n   <\/p>\n<h2>\n    \u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model = LinearRegression()\n   <\/p>\n<h2>\n    \u8bad\u7ec3\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model.fit(X<br \/>\n    <em><br \/>\n     train, y<br \/>\n    <\/em><br \/>\n    train)\n   <\/p>\n<h2>\n    \u9884\u6d4b<br \/>\n   <\/h2>\n<p>\n    y<br \/>\n    <em><br \/>\n     pred = model.predict(X<br \/>\n    <\/em><br \/>\n    test)\n   <\/p>\n<h2>\n    \u8bc4\u4f30\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    mse = mean<br \/>\n    <em><br \/>\n     squared<br \/>\n    <\/em><br \/>\n    error(y<br \/>\n    <em><br \/>\n     test, y<br \/>\n    <\/em><br \/>\n    pred) print(&#8216;MSE:&#8217;, mse) &#8220;`\n   <\/p>\n<h4>\n    1.4.2 \u652f\u6301\u5411\u91cf\u673a(SVM)<br \/>\n   <\/h4>\n<p>\n    &#8220;`python import numpy as np import pandas as pd from sklearn.svm import SVC from sklearn.model<br \/>\n    <em><br \/>\n     selection import train<br \/>\n    <\/em><br \/>\n    test<br \/>\n    <em><br \/>\n     split from sklearn.metrics import accuracy<br \/>\n    <\/em><br \/>\n    score\n   <\/p>\n<h2>\n    \u52a0\u8f7d\u6570\u636e<br \/>\n   <\/h2>\n<p>\n    data = pd.read<br \/>\n    <em><br \/>\n     csv(&#8216;training<br \/>\n    <\/em><br \/>\n    data.csv&#8217;)\n   <\/p>\n<h2>\n    \u9009\u53d6\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf<br \/>\n   <\/h2>\n<p>\n    X = data[[&#8216;heart<br \/>\n    <em><br \/>\n     rate&#8217;, &#8216;training<br \/>\n    <\/em><br \/>\n    load&#8217;, &#8216;fatigue<br \/>\n    <em><br \/>\n     level&#8217;]] y = data[&#8216;competition<br \/>\n    <\/em><br \/>\n    performance&#8217;]\n   <\/p>\n<h2>\n    \u8bad\u7ec3-\u6d4b\u8bd5\u6570\u636e\u96c6\u5206\u5272<br \/>\n   <\/h2>\n<p>\n    X<br \/>\n    <em><br \/>\n     train, X<br \/>\n    <\/em><br \/>\n    test, y<br \/>\n    <em><br \/>\n     train, y<br \/>\n    <\/em><br \/>\n    test = train<br \/>\n    <em><br \/>\n     test<br \/>\n    <\/em><br \/>\n    split(X, y, test<br \/>\n    <em><br \/>\n     size=0.2, random<br \/>\n    <\/em><br \/>\n    state=42)\n   <\/p>\n<h2>\n    \u521b\u5efa\u652f\u6301\u5411\u91cf\u673a\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model = SVC()\n   <\/p>\n<h2>\n    \u8bad\u7ec3\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model.fit(X<br \/>\n    <em><br \/>\n     train, y<br \/>\n    <\/em><br \/>\n    train)\n   <\/p>\n<h2>\n    \u9884\u6d4b<br \/>\n   <\/h2>\n<p>\n    y<br \/>\n    <em><br \/>\n     pred = model.predict(X<br \/>\n    <\/em><br \/>\n    test)\n   <\/p>\n<h2>\n    \u8bc4\u4f30\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    acc = accuracy<br \/>\n    <em><br \/>\n     score(y<br \/>\n    <\/em><br \/>\n    test, y_pred) print(&#8216;Accuracy:&#8217;, acc) &#8220;`\n   <\/p>\n<h4>\n    1.4.3 \u968f\u673a\u68ee\u6797<br \/>\n   <\/h4>\n<p>\n    &#8220;`python import numpy as np import pandas as pd from sklearn.ensemble import RandomForestRegressor from sklearn.model<br \/>\n    <em><br \/>\n     selection import train<br \/>\n    <\/em><br \/>\n    test<br \/>\n    <em><br \/>\n     split from sklearn.metrics import mean<br \/>\n    <\/em><br \/>\n    squared_error\n   <\/p>\n<h2>\n    \u52a0\u8f7d\u6570\u636e<br \/>\n   <\/h2>\n<p>\n    data = pd.read<br \/>\n    <em><br \/>\n     csv(&#8216;training<br \/>\n    <\/em><br \/>\n    data.csv&#8217;)\n   <\/p>\n<h2>\n    \u9009\u53d6\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf<br \/>\n   <\/h2>\n<p>\n    X = data[[&#8216;heart<br \/>\n    <em><br \/>\n     rate&#8217;, &#8216;training<br \/>\n    <\/em><br \/>\n    load&#8217;, &#8216;fatigue<br \/>\n    <em><br \/>\n     level&#8217;]] y = data[&#8216;competition<br \/>\n    <\/em><br \/>\n    performance&#8217;]\n   <\/p>\n<h2>\n    \u8bad\u7ec3-\u6d4b\u8bd5\u6570\u636e\u96c6\u5206\u5272<br \/>\n   <\/h2>\n<p>\n    X<br \/>\n    <em><br \/>\n     train, X<br \/>\n    <\/em><br \/>\n    test, y<br \/>\n    <em><br \/>\n     train, y<br \/>\n    <\/em><br \/>\n    test = train<br \/>\n    <em><br \/>\n     test<br \/>\n    <\/em><br \/>\n    split(X, y, test<br \/>\n    <em><br \/>\n     size=0.2, random<br \/>\n    <\/em><br \/>\n    state=42)\n   <\/p>\n<h2>\n    \u521b\u5efa\u968f\u673a\u68ee\u6797\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model = RandomForestRegressor()\n   <\/p>\n<h2>\n    \u8bad\u7ec3\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model.fit(X<br \/>\n    <em><br \/>\n     train, y<br \/>\n    <\/em><br \/>\n    train)\n   <\/p>\n<h2>\n    \u9884\u6d4b<br \/>\n   <\/h2>\n<p>\n    y<br \/>\n    <em><br \/>\n     pred = model.predict(X<br \/>\n    <\/em><br \/>\n    test)\n   <\/p>\n<h2>\n    \u8bc4\u4f30\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    mse = mean<br \/>\n    <em><br \/>\n     squared<br \/>\n    <\/em><br \/>\n    error(y<br \/>\n    <em><br \/>\n     test, y<br \/>\n    <\/em><br \/>\n    pred) print(&#8216;MSE:&#8217;, mse) &#8220;`\n   <\/p>\n<h4>\n    1.4.4 \u65f6\u95f4\u5e8f\u5217\u5206\u6790<br \/>\n   <\/h4>\n<p>\n    &#8220;`python import numpy as np import pandas as pd from statsmodels.tsa.arima_model import ARIMA\n   <\/p>\n<h2>\n    \u52a0\u8f7d\u6570\u636e<br \/>\n   <\/h2>\n<p>\n    data = pd.read<br \/>\n    <em><br \/>\n     csv(&#8216;training<br \/>\n    <\/em><br \/>\n    data.csv&#8217;)\n   <\/p>\n<h2>\n    \u9009\u53d6\u76ee\u6807\u53d8\u91cf<br \/>\n   <\/h2>\n<p>\n    y = data[&#8216;heart_rate&#8217;]\n   <\/p>\n<h2>\n    \u65f6\u95f4\u5e8f\u5217\u8f6c\u6362<br \/>\n   <\/h2>\n<p>\n    y = pd.Series(y) y = y.dropna() y = y.astype(&#8216;float32&#8217;)\n   <\/p>\n<h2>\n    \u521b\u5efaARIMA\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model = ARIMA(y, order=(1, 1, 1))\n   <\/p>\n<h2>\n    \u8bad\u7ec3\u6a21\u578b<br \/>\n   <\/h2>\n<p>\n    model_fit = model.fit(disp=0)\n   <\/p>\n<h2>\n    \u9884\u6d4b<br \/>\n   <\/h2>\n<p>\n    pred = model_fit.forecast(steps=1)\n   <\/p>\n<h2>\n    \u8f93\u51fa\u9884\u6d4b\u7ed3\u679c<br \/>\n   <\/h2>\n<p>\n    print(&#8216;Predicted:&#8217;, pred) &#8220;`\n   <\/p>\n<h3>\n    1.5 \u672a\u6765\u53d1\u5c55\u8d8b\u52bf\u4e0e\u6311\u6218<br \/>\n   <\/h3>\n<p>\n    \u968f\u7740\u79d1\u6280\u7684\u4e0d\u65ad\u53d1\u5c55\uff0c\u6570\u5b57\u5316\u4f53\u80b2\u7684\u5e94\u7528\u5c06\u4f1a\u66f4\u52a0\u5e7f\u6cdb\u3002\u672a\u6765\u7684\u8d8b\u52bf\u5305\u62ec\uff1a\n   <\/p>\n<ul>\n<li>\n     \u66f4\u52a0\u7cbe\u786e\u7684\u8bad\u7ec3\u6570\u636e\u6536\u96c6\u548c\u4f20\u8f93\uff0c\u5982\u8eab\u4f53\u4f20\u611f\u5668\u3001\u667a\u80fd\u624b\u673a\u7b49\u3002\n    <\/li>\n<li>\n     \u66f4\u52a0\u9ad8\u6548\u7684\u6570\u636e\u5206\u6790\u65b9\u6cd5\uff0c\u5982\u6df1\u5ea6\u5b66\u4e60\u3001\u751f\u7269\u5b66\u77e5\u8bc6\u8fc1\u79fb\u7b49\u3002\n    <\/li>\n<li>\n     \u66f4\u52a0\u4e2a\u6027\u5316\u7684\u8bad\u7ec3\u5efa\u8bae\uff0c\u6839\u636e\u8fd0\u52a8\u5458\u7684\u7279\u70b9\u548c\u9700\u6c42\u8fdb\u884c\u5b9a\u5236\u3002\n    <\/li>\n<\/ul>\n<p>\n    \u4f46\u662f\uff0c\u6570\u5b57\u5316\u4f53\u80b2\u7684\u53d1\u5c55\u4e5f\u9762\u4e34\u7740\u4e00\u4e9b\u6311\u6218\uff0c\u5982\uff1a\n   <\/p>\n<ul>\n<li>\n     \u6570\u636e\u9690\u79c1\u548c\u5b89\u5168\u95ee\u9898\uff0c\u5982\u8fd0\u52a8\u5458\u7684\u4e2a\u4eba\u4fe1\u606f\u6cc4\u9732\u7b49\u3002\n    <\/li>\n<li>\n     \u6570\u636e\u8d28\u91cf\u548c\u5b8c\u6574\u6027\u95ee\u9898\uff0c\u5982\u6570\u636e\u7f3a\u5931\u3001\u566a\u58f0\u7b49\u3002\n    <\/li>\n<li>\n     \u7b97\u6cd5\u89e3\u91ca\u548c\u53ef\u89e3\u91ca\u6027\u95ee\u9898\uff0c\u5982\u6a21\u578b\u7684\u89e3\u91ca\u96be\u4ee5\u7406\u89e3\u7b49\u3002\n    <\/li>\n<\/ul>\n<h3>\n    1.6 \u9644\u5f55\u5e38\u89c1\u95ee\u9898\u4e0e\u89e3\u7b54<br \/>\n   <\/h3>\n<h4>\n    Q1\uff1a\u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\uff1f<br \/>\n   <\/h4>\n<p>\n    A1\uff1a\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\u9700\u8981\u6839\u636e\u95ee\u9898\u7684\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u7279\u5f81\u6765\u51b3\u5b9a\u3002\u53ef\u4ee5\u901a\u8fc7\u5bf9\u6bd4\u4e0d\u540c\u7b97\u6cd5\u7684\u4f18\u7f3a\u70b9\u3001\u9002\u7528\u573a\u666f\u7b49\u8fdb\u884c\u9009\u62e9\u3002\n   <\/p>\n<h4>\n    Q2\uff1a\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u548c\u566a\u58f0\uff1f<br \/>\n   <\/h4>\n<p>\n    A2\uff1a\u53ef\u4ee5\u4f7f\u7528\u6570\u636e\u9884\u5904\u7406\u65b9\u6cd5\uff0c\u5982\u5220\u9664\u7f3a\u5931\u503c\u3001\u586b\u5145\u7f3a\u5931\u503c\u3001\u53bb\u566a\u5904\u7406\u7b49\uff0c\u6765\u5904\u7406\u7f3a\u5931\u503c\u548c\u566a\u58f0\u95ee\u9898\u3002\n   <\/p>\n<h4>\n    Q3\uff1a\u5982\u4f55\u4fdd\u62a4\u6570\u636e\u9690\u79c1\u548c\u5b89\u5168\uff1f<br \/>\n   <\/h4>\n<p>\n    A3\uff1a\u53ef\u4ee5\u4f7f\u7528\u6570\u636e\u52a0\u5bc6\u3001\u8bbf\u95ee\u63a7\u5236\u3001\u533f\u540d\u5904\u7406\u7b49\u65b9\u6cd5\uff0c\u6765\u4fdd\u62a4\u6570\u636e\u9690\u79c1\u548c\u5b89\u5168\u3002\n   <\/p>\n<h4>\n    Q4\uff1a\u5982\u4f55\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff1f<br \/>\n   <\/h4>\n<p>\n    A4\uff1a\u53ef\u4ee5\u4f7f\u7528\u6a21\u578b\u6027\u80fd\u6307\u6807\uff0c\u5982\u51c6\u786e\u7387\u3001\u5747\u65b9\u8bef\u5dee\u3001AUC\u7b49\uff0c\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\n   <\/p>\n<h4>\n    Q5\uff1a\u5982\u4f55\u8fdb\u884c\u6a21\u578b\u89e3\u91ca\u548c\u53ef\u89e3\u91ca\u6027\uff1f<br \/>\n   <\/h4>\n<p>\n    A5\uff1a\u53ef\u4ee5\u4f7f\u7528\u6a21\u578b\u89e3\u91ca\u65b9\u6cd5\uff0c\u5982\u7279\u5f81\u91cd\u8981\u6027\u3001\u51b3\u7b56\u6811\u53ef\u89c6\u5316\u7b49\uff0c\u6765\u8fdb\u884c\u6a21\u578b\u89e3\u91ca\u548c\u53ef\u89e3\u91ca\u6027\u5206\u6790\u3002\n   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