update decisiontree regression problem details
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<div class="flink-item-name">Youtube</div>
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<div class="flink-item-name">Weibo</div>
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<div class="flink-item-desc" title="中國最大社交分享平台">中國最大社交分享平台</div>
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})(window)</script><meta name="generator" content="Hexo 7.3.0"></head><body><div id="web_bg"></div><div id="sidebar"><div id="menu-mask"></div><div id="sidebar-menus"><div class="avatar-img is-center"><img src="/img/avatar.jpg" onerror="onerror=null;src='/img/friend_404.gif'" alt="avatar"/></div><div class="sidebar-site-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">17</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">8</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><hr class="custom-hr"/><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</span></a></div></div></div></div><div class="page" id="body-wrap"><header class="full_page fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><nav id="nav"><span id="blog-info"><a href="/" title="QuickReference"><span class="site-name">QuickReference</span></a></span><div id="menus"><div id="search-button"><a class="site-page social-icon search" href="javascript:void(0);"><i class="fas fa-search fa-fw"></i><span> 搜索</span></a></div><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</span></a></div></div><div id="toggle-menu"><a class="site-page" href="javascript:void(0);"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="site-info"><h1 id="site-title">QuickReference</h1><div id="site-subtitle"><span id="subtitle"></span></div><div id="site_social_icons"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></i></a></div></div><div id="scroll-down"><i class="fas fa-angle-down scroll-down-effects"></i></div></header><main class="layout" id="content-inner"><div class="recent-posts" id="recent-posts"><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/58638.html" title="郑伯克段于鄢">郑伯克段于鄢</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-08-09T12:00:13.000Z" title="发表于 2024-08-09 20:00:13">2024-08-09</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/">古文观止</a></span></div><div class="content">原文如下：
|
||||
初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。
|
||||
</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/41168.html" title="网络相关">网络相关</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-08-07T02:06:08.000Z" title="发表于 2024-08-07 10:06:08">2024-08-07</time></span></div><div class="content"></div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/14011.html" title="无法访问外网？需要订阅代理服务？">无法访问外网？需要订阅代理服务？</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-08-07T02:06:08.000Z" title="发表于 2024-08-07 10:06:08">2024-08-07</time></span></div><div class="content">由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。
|
||||
|
||||
|
||||
介绍
|
||||
根据测试，许多提供服务的提供商所在的网站需要使用外部网络才能打开，仅有少部分的网站(比较贵)可以直接使用国内网络环境打开直接购买订阅服务。
|
||||
|
||||
那么你现在可以有两个选择：
|
||||
|
||||
方案一：使用无需外部网络便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。
|
||||
方案二：如果使用此方案，详见下方。
|
||||
|
||||
解决方案
|
||||
采用方案二方式
|
||||
这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)
|
||||
此教程中我们使用的机场是
|
||||
|
||||
一元机场: https://xn--4gq62f52gdss.com/
|
||||
赔钱机场：https://xn--mes358aby2apfg.com/
|
||||
|
||||
|
||||
机场选择的建议：
|
||||
一元机场可以看到
|
||||
|
||||
12元/年,每月50GB的可用额 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/33957.html" title="组件使用">组件使用</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-08-05T06:07:01.000Z" title="发表于 2024-08-05 14:07:01">2024-08-05</time></span></div><div class="content">组件自动导入123456"easycom":{ "autoscan": true, "custom": { "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue" // 匹配components目录内的vue文件 }}
|
||||
|
||||
tui-sticky 吸顶容器
|
||||
包含 以下 tui 组件 :
|
||||
|
|
@ -161,7 +186,10 @@ tui-list-view
|
|||
tui-list-cell
|
||||
|
||||
|
||||
123456789101112131415161718192021222324252627282930<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container> <!-- header start --> <template v-slot:header> ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/47807.html" title="Docker被墙，如何继续使用？">Docker被墙，如何继续使用？</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-08-01T01:10:40.000Z" title="发表于 2024-08-01 09:10:40">2024-08-01</time></span></div><div class="content">Docker Download
|
||||
123456789101112131415161718192021222324252627282930<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container> <!-- header start --> <template v-slot:header> ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/58817.html" title="uniapp 开发">uniapp 开发</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-08-05T06:07:01.000Z" title="发表于 2024-08-05 14:07:01">2024-08-05</time></span></div><div class="content">
|
||||
uniapp component
|
||||
|
||||
</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/posts/47807.html" title="Docker被墙，如何继续使用？">Docker被墙，如何继续使用？</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-08-01T01:10:40.000Z" title="发表于 2024-08-01 09:10:40">2024-08-01</time></span></div><div class="content">Docker Download
|
||||
自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。解决docker安装问题：https://github.com/shenjianZ/docker_installer
|
||||
|
||||
|
||||
|
|
@ -184,14 +212,14 @@ Docker pull images
|
|||
deploy
|
||||
|
||||
|
||||
</div></div></div><nav id="pagination"><div class="pagination"><a class="extend prev" rel="prev" href="/"><i class="fas fa-chevron-left fa-fw"></i></a><a class="page-number" href="/">1</a><span class="page-number current">2</span></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">13</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">4</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">1</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/29139.html" title="k近邻算法（K-Nearest Neighbors）KNN">k近邻算法（K-Nearest Neighbors）KNN</a><time datetime="2025-01-13T09:20:59.000Z" title="发表于 2025-01-13 17:20:59">2025-01-13</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/61253.html" title="Hadoop集群搭建基础环境">Hadoop集群搭建基础环境</a><time datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/61252.html" title="Hadoop集群HDFS配置">Hadoop集群HDFS配置</a><time datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/61251.html" title="Hadoop集群Zookeeper配置">Hadoop集群Zookeeper配置</a><time datetime="2024-09-11T14:45:40.000Z" title="发表于 2024-09-11 22:45:40">2024-09-11</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/16107.html" title="Hello World">Hello World</a><time datetime="2024-09-11T00:01:10.419Z" title="发表于 2024-09-11 08:01:10">2024-09-11</time></div></div></div></div><div class="card-widget card-categories"><div class="item-headline">
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</div></div></div><nav id="pagination"><div class="pagination"><a class="extend prev" rel="prev" href="/"><i class="fas fa-chevron-left fa-fw"></i></a><a class="page-number" href="/">1</a><span class="page-number current">2</span></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">shenjianZ</div><div class="author-info__description">一份快捷简便的文档，便于查阅编程的细节</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">17</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">8</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/shenjianz"><i class="fab fa-github"></i><span>Follow Me</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/shenjianZ" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:15202078626@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">一个简单快捷的文档知识点查阅网站</div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/95.html" title="决策树算法">决策树算法</a><time datetime="2025-01-24T04:39:59.000Z" title="发表于 2025-01-24 12:39:59">2025-01-24</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/60504.html" title="逻辑回归">逻辑回归</a><time datetime="2025-01-20T07:30:08.000Z" title="发表于 2025-01-20 15:30:08">2025-01-20</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/52662.html" title="线性回归">线性回归</a><time datetime="2025-01-19T08:46:51.000Z" title="发表于 2025-01-19 16:46:51">2025-01-19</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/12462.html" title="C lang">C lang</a><time datetime="2025-01-15T12:41:26.000Z" title="发表于 2025-01-15 20:41:26">2025-01-15</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/posts/29139.html" title="k近邻算法（K-Nearest Neighbors）KNN">k近邻算法（K-Nearest Neighbors）KNN</a><time datetime="2025-01-13T09:20:59.000Z" title="发表于 2025-01-13 17:20:59">2025-01-13</time></div></div></div></div><div class="card-widget card-categories"><div class="item-headline">
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<i class="fas fa-folder-open"></i>
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<span>分类</span>
|
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|
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</div>
|
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<ul class="card-category-list" id="aside-cat-list">
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/machinelearning/" style="font-size: 1.1em; color: #999">machinelearning</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a> <a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">1</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">13</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-01-14T09:22:51.454Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><span>备案号：豫ICP备2023019300号</span></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"><script>window.typedJSFn = {
|
||||
<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">4</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
|
||||
</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">5</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">17</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-01-24T10:17:07.459Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><span>备案号：豫ICP备2023019300号</span></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"><script>window.typedJSFn = {
|
||||
init: (str) => {
|
||||
window.typed = new Typed('#subtitle', Object.assign({
|
||||
strings: str,
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
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|
|
@ -1,22 +1,22 @@
|
|||
- class_name: 友情鏈接
|
||||
- class_name: 友情链接
|
||||
class_desc: 那些人，那些事
|
||||
link_list:
|
||||
- name: Hexo
|
||||
link: https://hexo.io/zh-tw/
|
||||
link: https://hexo.io/zh-cn/
|
||||
avatar: https://d33wubrfki0l68.cloudfront.net/6657ba50e702d84afb32fe846bed54fba1a77add/827ae/logo.svg
|
||||
descr: 快速、簡單且強大的網誌框架
|
||||
descr: 快速、简单且強大的文档框架
|
||||
|
||||
- class_name: 網站
|
||||
class_desc: 值得推薦的網站
|
||||
- class_name: 网站
|
||||
class_desc: 值得推荐的网站
|
||||
link_list:
|
||||
- name: Youtube
|
||||
link: https://www.youtube.com/
|
||||
avatar: https://i.loli.net/2020/05/14/9ZkGg8v3azHJfM1.png
|
||||
descr: 視頻網站
|
||||
descr: 视频网站
|
||||
- name: Weibo
|
||||
link: https://www.weibo.com/
|
||||
avatar: https://i.loli.net/2020/05/14/TLJBum386vcnI1P.png
|
||||
descr: 中國最大社交分享平台
|
||||
descr: 中国最大社交分享平台
|
||||
- name: Twitter
|
||||
link: https://twitter.com/
|
||||
avatar: https://i.loli.net/2020/05/14/5VyHPQqR6LWF39a.png
|
||||
|
|
|
|||
|
|
@ -0,0 +1,81 @@
|
|||
---
|
||||
title: C lang
|
||||
tags: C C++
|
||||
abbrlink: 12462
|
||||
date: 2025-01-15 20:41:26
|
||||
---
|
||||
|
||||
### c lang在windows下的开发（VS code）
|
||||
[WinLibs - GCC+MinGW-w64 compiler for Windows](https://winlibs.com/#download-release)下载你需要的版本
|
||||
解压到`D:\ProgramModule`，并将 `bin\`加入环境变量`PATH`
|
||||
打开新的`Terminal`输入`gcc -v`，查看`gcc`是否安装成功
|
||||
在`VS code` 的插件管理下载`Code Runner`、`C\C++`这两个插件
|
||||
在`*.c`源文件的内容区，右键点击`Run Code` ，即可运行成功
|
||||
|
||||
|
||||
### 数据类型
|
||||
- 整数类型
|
||||
```c
|
||||
short a = 12;
|
||||
int b = 100;
|
||||
long c = 1000L;
|
||||
long long d = 1000000LL;
|
||||
unsigned int e = 10;
|
||||
printf("a: %hd\n",a);
|
||||
printf("b: %d\n",b);
|
||||
printf("c: %ld\n",c);
|
||||
printf("d: %lld\n",d);
|
||||
printf("e: %u\n",e);
|
||||
printf("f: %.3f\n",f);
|
||||
```
|
||||
- 小数类型
|
||||
```c
|
||||
float f = 3.14F;
|
||||
printf("f: %.3f\n",f);
|
||||
double g = 5.65;
|
||||
printf("g: %.2lf\n",g);
|
||||
```
|
||||
- 字符类型
|
||||
```c
|
||||
char h = 'x';
|
||||
printf("x: %c\n",x);
|
||||
```
|
||||
### 类型转换
|
||||
- 隐式转换
|
||||
- 强制转换
|
||||
```c
|
||||
int b = 23;
|
||||
short c = (short) b;
|
||||
```
|
||||
### 数组
|
||||
```c
|
||||
#include <stdio.h>
|
||||
|
||||
int main(){
|
||||
int arr [10] = {2,3,4,5,6,7,8,9,10,11};
|
||||
arr[0] = 1525;
|
||||
*(arr+1) = 25;
|
||||
int len = sizeof(arr)/sizeof(arr[0]);
|
||||
void printArr(int arr[], int len){
|
||||
for (int i = 0; i < len;i++){
|
||||
printf("%d\t",arr[i]);
|
||||
}
|
||||
}
|
||||
printArr(arr,len);
|
||||
return 0;
|
||||
}
|
||||
```
|
||||
### 指针
|
||||
```c
|
||||
// swap the value of a and b
|
||||
void swap(int* x, int* y){
|
||||
int temp = *x;
|
||||
*x = *y;
|
||||
*y = temp;
|
||||
|
||||
}
|
||||
int a = 5;
|
||||
int b = 10;
|
||||
swap(&a, &b);
|
||||
printf("a = %d b = %d\n", a, b);
|
||||
```
|
||||
|
|
@ -106,6 +106,7 @@ print(f"转换后的数据:\n{new_data}")
|
|||
|
||||
|
||||
### 回归决策树
|
||||
<<<<<<< HEAD
|
||||
#### 决策树算法的应用 （泰坦尼克号沉船幸存者预测）
|
||||
```python
|
||||
import seaborn as sns
|
||||
|
|
@ -168,6 +169,9 @@ graph.view(output_path) # 打开图像，path为保存路径，不需要加后
|
|||
```
|
||||
|
||||
[Webgraphviz](http://webgraphviz.com/)，这个网站可以将`tree.dot`文件的内容生成对应的可视化树
|
||||
=======
|
||||
[Webgraphviz](http://webgraphviz.com/)
|
||||
>>>>>>> efff4fc1310b4d2f201748d9b976f5efbb4a42bf
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
---
|
||||
title: k近邻算法（K-Nearest Neighbors）KNN
|
||||
tags: machinelearning
|
||||
tags: KNN
|
||||
categories: machinelearning
|
||||
abbrlink: 29139
|
||||
mathjax: true
|
||||
date: 2025-01-13 17:20:59
|
||||
|
|
|
|||
|
|
@ -0,0 +1,200 @@
|
|||
---
|
||||
title: 线性回归
|
||||
tags: linear-regression
|
||||
categories: machinelearning
|
||||
mathjax: true
|
||||
abbrlink: 52662
|
||||
date: 2025-01-19 16:46:51
|
||||
---
|
||||
|
||||
### 线性回归简介
|
||||
>用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。
|
||||
|
||||
假设函数：
|
||||
$$y = w_1x_1 + w_2x_2 + \cdot\cdot\cdot+w_nx_n$$
|
||||
- $y$ 是目标变量（因变量），即我们希望预测的值。
|
||||
- $x1,x2,…,xn$ 是特征变量（自变量），即输入的值。
|
||||
### 损失函数
|
||||
|
||||
为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 **均方误差（MSE）**：
|
||||
$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$
|
||||
- m 是样本的数量。
|
||||
- $y_i$ 是第 i 个样本的真实值。
|
||||
- $\hat{y}_i$ 是模型预测的第 i 个样本的值。
|
||||
|
||||
### 线性回归优化
|
||||
|
||||
- 梯度下降法
|
||||
```python
|
||||
from sklearn.datasets import fetch_california_housing
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.linear_model import SGDRegressor
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
# 1. 获取数据集
|
||||
housing = fetch_california_housing()
|
||||
|
||||
# 2. 数据集处理
|
||||
# 2.1 分割数据集
|
||||
X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
|
||||
|
||||
# 3. 特征工程
|
||||
# 3.1 标准化
|
||||
transfer = StandardScaler()
|
||||
X_train = transfer.fit_transform(X_train)
|
||||
X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()
|
||||
|
||||
# 4.机器学习- 梯度下降法
|
||||
estimater = SGDRegressor(max_iter=1000, eta0=0.01)
|
||||
estimater.fit(X_train, y_train)
|
||||
print(f"SGD模型的偏置是：{estimater.intercept_}")
|
||||
print(f"SGD模型的系数是：{estimater.coef_}")
|
||||
|
||||
# 5. 模型评估
|
||||
y_pred = estimater.predict(X_test)
|
||||
print(f"SGD模型预测值：{y_pred}")
|
||||
mse = mean_squared_error(y_test, y_pred)
|
||||
print(f"SGD模型均方误差:{mse}")
|
||||
```
|
||||
|
||||
- 正规方程
|
||||
```python
|
||||
from sklearn.datasets import fetch_california_housing
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
# 1. 获取数据集
|
||||
housing = fetch_california_housing()
|
||||
|
||||
# 2. 数据集处理
|
||||
# 2.1 分割数据集
|
||||
X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
|
||||
|
||||
# 3. 特征工程
|
||||
# 3.1 标准化
|
||||
transfer = StandardScaler()
|
||||
X_train = transfer.fit_transform(X_train)
|
||||
X_test = transfer.fit_transform(X_test)
|
||||
|
||||
# 4.机器学习- 线性回归
|
||||
estimater = LinearRegression()
|
||||
estimater.fit(X_train, y_train)
|
||||
print(f"模型的偏置是：{estimater.intercept_}")
|
||||
print(f"模型的系数是：{estimater.coef_}")
|
||||
|
||||
# 5. 模型评估
|
||||
y_pred = estimater.predict(X_test)
|
||||
print(f"模型预测值：{y_pred}")
|
||||
mse = mean_squared_error(y_test, y_pred)
|
||||
print(f"模型均方误差:{mse}")
|
||||
```
|
||||
|
||||
- 岭回归
|
||||
```python
|
||||
from sklearn.datasets import fetch_california_housing
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.linear_model import Ridge, RidgeCV
|
||||
from sklearn.metrics import mean_squared_error
|
||||
|
||||
# 1. 获取数据集
|
||||
housing = fetch_california_housing()
|
||||
|
||||
# 2. 数据集处理
|
||||
# 2.1 分割数据集
|
||||
X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
|
||||
|
||||
# 3. 特征工程
|
||||
# 3.1 标准化
|
||||
transfer = StandardScaler()
|
||||
X_train = transfer.fit_transform(X_train)
|
||||
X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()
|
||||
|
||||
# 4.机器学习- 岭回归 使用了Ridge的alpha的搜索
|
||||
# estimater = Ridge(alpha=1.0)
|
||||
estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
|
||||
estimater.fit(X_train, y_train)
|
||||
print(f"Ridge模型的偏置是：{estimater.intercept_}")
|
||||
print(f"Ridge模型的系数是：{estimater.coef_}")
|
||||
|
||||
# 查看最佳 alpha
|
||||
print(f"最佳 alpha 值是：{estimater.alpha_}")
|
||||
|
||||
# 5. 模型评估
|
||||
y_pred = estimater.predict(X_test)
|
||||
print(f"Ridge模型预测值：{y_pred}")
|
||||
mse = mean_squared_error(y_test, y_pred)
|
||||
print(f"Ridge模型均方误差:{mse}")
|
||||
```
|
||||
|
||||
这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
### 模型保存和加载
|
||||
```python
|
||||
from sklearn.datasets import fetch_california_housing
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.linear_model import Ridge, RidgeCV
|
||||
from sklearn.metrics import mean_squared_error
|
||||
import joblib
|
||||
|
||||
def save_model():
|
||||
# 1. 获取数据集
|
||||
housing = fetch_california_housing()
|
||||
# 2. 数据集处理
|
||||
# 2.1 分割数据集
|
||||
X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
|
||||
# 3. 特征工程
|
||||
# 3.1 标准化
|
||||
transfer = StandardScaler()
|
||||
X_train = transfer.fit_transform(X_train)
|
||||
X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()
|
||||
# 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索
|
||||
estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
|
||||
estimater.fit(X_train, y_train)
|
||||
print(f"Ridge模型的偏置是：{estimater.intercept_}")
|
||||
print(f"Ridge模型的系数是：{estimater.coef_}")
|
||||
# 保存模型
|
||||
joblib.dump(estimater, 'ridge_model.pkl')
|
||||
# 查看最佳 alpha
|
||||
print(f"最佳 alpha 值是：{estimater.alpha_}")
|
||||
# 5. 模型评估
|
||||
y_pred = estimater.predict(X_test)
|
||||
mse = mean_squared_error(y_test, y_pred)
|
||||
print(f"Ridge模型均方误差:{mse}")
|
||||
|
||||
def load_model():
|
||||
# 1. 获取数据集
|
||||
housing = fetch_california_housing()
|
||||
# 2. 数据集处理
|
||||
# 2.1 分割数据集
|
||||
X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
|
||||
# 3. 特征工程
|
||||
# 3.1 标准化
|
||||
transfer = StandardScaler()
|
||||
X_train = transfer.fit_transform(X_train)
|
||||
X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()
|
||||
# 加载模型
|
||||
estimater = joblib.load('ridge_model.pkl')
|
||||
print(f"Ridge模型的偏置是：{estimater.intercept_}")
|
||||
print(f"Ridge模型的系数是：{estimater.coef_}")
|
||||
# 查看最佳 alpha
|
||||
print(f"最佳 alpha 值是：{estimater.alpha_}")
|
||||
# 5. 模型评估
|
||||
y_pred = estimater.predict(X_test)
|
||||
mse = mean_squared_error(y_test, y_pred)
|
||||
print(f"Ridge模型预测值：{y_pred}")
|
||||
print(f"Ridge模型均方误差:{mse}")
|
||||
|
||||
print("训练并保存模型：")
|
||||
save_model()
|
||||
print("加载模型")
|
||||
load_model()
|
||||
```
|
||||
|
|
@ -0,0 +1,173 @@
|
|||
---
|
||||
title: 逻辑回归
|
||||
tags: logistic-regression
|
||||
categories: machinelearning
|
||||
mathjax: true
|
||||
abbrlink: 60504
|
||||
date: 2025-01-20 15:30:08
|
||||
---
|
||||
|
||||
### logistic regression code
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.datasets import load_breast_cancer
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
# 1. 加载乳腺癌数据集
|
||||
data = load_breast_cancer()
|
||||
# 2.1 数据集基本处理
|
||||
df = pd.DataFrame(data.data, columns=data.feature_names)
|
||||
df['target'] = data.target
|
||||
for i in df.columns:
|
||||
# 检查列是否有缺失值
|
||||
if np.any(pd.isnull(df[i])):
|
||||
print(f"Filling missing values in column: {i}")
|
||||
#2.2 确认特征值、目标值
|
||||
X = df.iloc[:,0:df.shape[1] - 1]
|
||||
y = df.loc[:,"target"]
|
||||
# 2.3 分割数据
|
||||
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)
|
||||
# 显示前几行数据
|
||||
df.head(1)
|
||||
|
||||
# 3. 特征工程 标准化
|
||||
transfer = StandardScaler()
|
||||
X_train = transfer.fit_transform(X_train)
|
||||
X_test = transfer.transform(X_test)
|
||||
|
||||
# 4 机器学习 逻辑回归
|
||||
estimator = LogisticRegression()
|
||||
estimator.fit(X_train,y_train)
|
||||
|
||||
# 5. 模型评估
|
||||
print(f"模型准确率：{estimator.score(X_test,y_test)}")
|
||||
print(f"模型预测值为:\n{estimator.predict(X_test)}")
|
||||
```
|
||||
|
||||
### 分类评估的参数
|
||||
- 准确率
|
||||
准确率是所有预测正确的样本占总样本的比例
|
||||
$$Accuracy = \frac{TP+TN}{TP+FN+FP+TN}$$
|
||||
|
||||
- 精准率
|
||||
精准率（又称查准率）是指所有被预测为正类的样本中，真正为正类的比例
|
||||
$$Precision = \frac{TP}{TP+FP}$$
|
||||
|
||||
- 召回率
|
||||
召回率（又称查全率）是指所有实际为正类的样本中，被正确预测为正类的比例
|
||||
$$Recall = \frac{TP}{TP+FN}$$
|
||||
|
||||
- F1-score
|
||||
F1 值（F1 Score）是精准率和召回率的调和平均数，综合考虑了精准率和召回率的影响。
|
||||
$$ F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} $$
|
||||
|
||||
- roc曲线
|
||||
tpr、fpr来衡量不平衡的二分类问题
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.datasets import load_breast_cancer
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.metrics import classification_report, roc_auc_score
|
||||
# 1. 加载乳腺癌数据集
|
||||
data = load_breast_cancer()
|
||||
# 2.1 数据集基本处理
|
||||
df = pd.DataFrame(data.data, columns=data.feature_names)
|
||||
df['target'] = data.target
|
||||
for i in df.columns:
|
||||
# 检查列是否有缺失值
|
||||
if np.any(pd.isnull(df[i])):
|
||||
print(f"Filling missing values in column: {i}")
|
||||
# 2.2 确认特征值、目标值
|
||||
X = df.iloc[:, 0:df.shape[1] - 1]
|
||||
y = df.loc[:, "target"]
|
||||
# 2.3 分割数据
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
|
||||
# 显示前几行数据
|
||||
df.head(1)
|
||||
|
||||
# 3. 特征工程 标准化
|
||||
transfer = StandardScaler()
|
||||
X_train = transfer.fit_transform(X_train)
|
||||
X_test = transfer.transform(X_test)
|
||||
|
||||
# 4 机器学习 逻辑回归
|
||||
estimator = LogisticRegression()
|
||||
estimator.fit(X_train, y_train)
|
||||
|
||||
# 5. 模型评估
|
||||
print(f"模型准确率：{estimator.score(X_test, y_test)}")
|
||||
y_pred = estimator.predict(X_test)
|
||||
print(f"模型预测值为:\n{y_pred}")
|
||||
# 5.1 精确率、召回率
|
||||
ret = classification_report(y_test, y_pred, labels=[1, 0], target_names=["良性", "恶性"])
|
||||
roc_score = roc_auc_score(y_test, y_pred)
|
||||
print(f"准确率、召回率：{ret}")
|
||||
print(f"roc_score:{roc_score}")
|
||||
```
|
||||
|
||||
### 类别不平衡的处理
|
||||
先准备类别不平衡的数据
|
||||
|
||||
```python
|
||||
from imblearn.over_sampling import RandomOverSampler,SMOTE
|
||||
from imblearn.under_sampling import RandomUnderSampler
|
||||
from sklearn.datasets import make_classification
|
||||
import matplotlib.pyplot as plt
|
||||
from collections import Counter
|
||||
|
||||
# 1.准备类别不平衡的数据
|
||||
X, y = make_classification(
|
||||
n_samples=5000,
|
||||
n_features=2,
|
||||
n_informative=2,
|
||||
n_redundant=0,
|
||||
n_repeated=0,
|
||||
n_classes=3,
|
||||
n_clusters_per_class=1,
|
||||
weights=[0.01, 0.05, 0.94],
|
||||
random_state=0,
|
||||
)
|
||||
counter = Counter(y)
|
||||
plt.scatter(X[:,0],X[:,1],c=y)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
- 过采样
|
||||
增加训练集的少数的类别的样本，使得正反例样本数据接近
|
||||
- 随机过采样（RandomOverSampler)
|
||||
```python
|
||||
ros = RandomOverSampler()
|
||||
X_resampled,y_resampled = ros.fit_resample(X,y)
|
||||
print(Counter(y_resampled))
|
||||
plt.scatter(X_resampled[:,0],X_resampled[:,1],c=y_resampled)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
- `SMOTE`过采样（SMOTE）
|
||||
```python
|
||||
smote = SMOTE()
|
||||
X_resampled,y_resampled = smote.fit_resample(X,y)
|
||||
print(Counter(y_resampled))
|
||||
plt.scatter(X_resampled[:,0],X_resampled[:,1],c=y_resampled)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
- 欠采样
|
||||
减少训练集的多数的类别的样本，使得正反例样本数据接近
|
||||
- 随机欠采样（RandomUnderSampler）
|
||||
```python
|
||||
rus = RandomUnderSampler(random_state=0)
|
||||
X_resampled,y_resampled = rus.fit_resample(X,y)
|
||||
print(Counter(y_resampled))
|
||||
plt.scatter(X_resampled[:,0],X_resampled[:,1],c=y_resampled)
|
||||
plt.show()
|
||||
```
|
||||
|
||||
|
||||
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Reference in New Issue