Compare commits
13 Commits
875fb417a7
...
master
| Author | SHA1 | Date | |
|---|---|---|---|
| a699cd8439 | |||
| 0bb27b4b85 | |||
| 49572eb3a0 | |||
| 8b240e8654 | |||
| 7cfd111895 | |||
| 14a299f13f | |||
| bcf66aee48 | |||
| 92fa085399 | |||
| 30ad5027db | |||
| 6b5d9f232e | |||
| efff4fc131 | |||
| dc75bd64f1 | |||
| 5ea5e3cba1 |
@@ -79,9 +79,9 @@ category_per_img: /img/site01.jpg
|
|||||||
|
|
||||||
cover:
|
cover:
|
||||||
# display the cover or not (是否顯示文章封面)
|
# display the cover or not (是否顯示文章封面)
|
||||||
index_enable: false
|
index_enable: true
|
||||||
aside_enable: false
|
aside_enable: true
|
||||||
archives_enable: false
|
archives_enable: true
|
||||||
# the position of cover in home page (封面顯示的位置)
|
# the position of cover in home page (封面顯示的位置)
|
||||||
# left/right/both
|
# left/right/both
|
||||||
position: both
|
position: both
|
||||||
@@ -208,7 +208,7 @@ footer:
|
|||||||
owner:
|
owner:
|
||||||
enable: true
|
enable: true
|
||||||
since: 2024
|
since: 2024
|
||||||
custom_text: <span>备案号：豫ICP备2023019300号</span>
|
custom_text: <a href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a>
|
||||||
|
|
||||||
# aside (側邊欄)
|
# aside (側邊欄)
|
||||||
# --------------------------------------
|
# --------------------------------------
|
||||||
|
|||||||
@@ -153,15 +153,15 @@
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
detectApple()
|
detectApple()
|
||||||
})(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">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><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="not-home-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="page-site-info"><h1 id="site-title">About me</h1></div></header><main class="layout" id="content-inner"><div id="page"><div id="article-container"><p>落花飞舞，翩若惊鸿。</p>
|
})(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">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</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="not-home-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="page-site-info"><h1 id="site-title">About me</h1></div></header><main class="layout" id="content-inner"><div id="page"><div id="article-container"><p>落花飞舞，翩若惊鸿。</p>
|
||||||
</div></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">
|
</div></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">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</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"><a class="thumbnail" href="/posts/8816.html" title="集成学习"><img src="/img/machinelearning/ensemble-learning.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="集成学习"/></a><div class="content"><a class="title" href="/posts/8816.html" title="集成学习">集成学习</a><time datetime="2025-01-25T07:12:08.000Z" title="发表于 2025-01-25 15:12:08">2025-01-25</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/posts/95.html" title="决策树算法"><img src="/img/machinelearning/decision-tree.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="决策树算法"/></a><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></div><div class="card-widget card-categories"><div class="item-headline">
|
||||||
<i class="fas fa-folder-open"></i>
|
<i class="fas fa-folder-open"></i>
|
||||||
<span>分类</span>
|
<span>分类</span>
|
||||||
|
|
||||||
</div>
|
</div>
|
||||||
<ul class="card-category-list" id="aside-cat-list">
|
<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>
|
<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">5</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/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"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
</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/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</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/%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/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</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/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</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">6</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">18</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="2025-10-16T03:29:44.082Z"><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"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></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"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
||||||
POWERMODE.shake = true;
|
POWERMODE.shake = true;
|
||||||
POWERMODE.mobile = false;
|
POWERMODE.mobile = false;
|
||||||
document.body.addEventListener('input', POWERMODE);
|
document.body.addEventListener('input', POWERMODE);
|
||||||
|
|||||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
158
public/categories/machinelearning/index.html
Normal file
158
public/categories/machinelearning/index.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
BIN
public/img/language/c-env-conf.png
Normal file
BIN
public/img/language/c-env-conf.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 126 KiB |
BIN
public/img/machinelearning/decision-tree.png
Normal file
BIN
public/img/machinelearning/decision-tree.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 537 KiB |
BIN
public/img/machinelearning/ensemble-learning.png
Normal file
BIN
public/img/machinelearning/ensemble-learning.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 613 KiB |
BIN
public/img/machinelearning/fitting.png
Normal file
BIN
public/img/machinelearning/fitting.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 100 KiB |
BIN
public/img/machinelearning/linear.png
Normal file
BIN
public/img/machinelearning/linear.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 123 KiB |
BIN
public/img/machinelearning/ott.png
Normal file
BIN
public/img/machinelearning/ott.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 53 KiB |
BIN
public/img/machinelearning/over_random_sampling.png
Normal file
BIN
public/img/machinelearning/over_random_sampling.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 29 KiB |
BIN
public/img/machinelearning/over_smote_sampling.png
Normal file
BIN
public/img/machinelearning/over_smote_sampling.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 39 KiB |
BIN
public/img/machinelearning/random-forest.png
Normal file
BIN
public/img/machinelearning/random-forest.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 116 KiB |
BIN
public/img/machinelearning/under_sampling.png
Normal file
BIN
public/img/machinelearning/under_sampling.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 36 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 266 KiB After Width: | Height: | Size: 770 KiB |
File diff suppressed because one or more lines are too long
@@ -153,23 +153,23 @@
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
detectApple()
|
detectApple()
|
||||||
})(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">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><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="not-home-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="page-site-info"><h1 id="site-title">link</h1></div></header><main class="layout" id="content-inner"><div id="page"><div id="article-container"><div class="flink"><h2 id="友情鏈接"><a href="#友情鏈接" class="headerlink" title="友情鏈接"></a>友情鏈接</h2><div class="flink-desc">那些人，那些事</div> <div class="flink-list">
|
})(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">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</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="not-home-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="page-site-info"><h1 id="site-title">link</h1></div></header><main class="layout" id="content-inner"><div id="page"><div id="article-container"><div class="flink"><h2 id="友情链接"><a href="#友情链接" class="headerlink" title="友情链接"></a>友情链接</h2><div class="flink-desc">那些人，那些事</div> <div class="flink-list">
|
||||||
<div class="flink-list-item">
|
<div class="flink-list-item">
|
||||||
<a href="https://hexo.io/zh-tw/" title="Hexo" target="_blank">
|
<a href="https://hexo.io/zh-cn/" title="Hexo" target="_blank">
|
||||||
<div class="flink-item-icon">
|
<div class="flink-item-icon">
|
||||||
<img class="no-lightbox" src="https://d33wubrfki0l68.cloudfront.net/6657ba50e702d84afb32fe846bed54fba1a77add/827ae/logo.svg" onerror='this.onerror=null;this.src="/img/friend_404.gif"' alt="Hexo" />
|
<img class="no-lightbox" src="https://d33wubrfki0l68.cloudfront.net/6657ba50e702d84afb32fe846bed54fba1a77add/827ae/logo.svg" onerror='this.onerror=null;this.src="/img/friend_404.gif"' alt="Hexo" />
|
||||||
</div>
|
</div>
|
||||||
<div class="flink-item-name">Hexo</div>
|
<div class="flink-item-name">Hexo</div>
|
||||||
<div class="flink-item-desc" title="快速、簡單且強大的網誌框架">快速、簡單且強大的網誌框架</div>
|
<div class="flink-item-desc" title="快速、简单且強大的文档框架">快速、简单且強大的文档框架</div>
|
||||||
</a>
|
</a>
|
||||||
</div></div><h2 id="網站"><a href="#網站" class="headerlink" title="網站"></a>網站</h2><div class="flink-desc">值得推薦的網站</div> <div class="flink-list">
|
</div></div><h2 id="网站"><a href="#网站" class="headerlink" title="网站"></a>网站</h2><div class="flink-desc">值得推荐的网站</div> <div class="flink-list">
|
||||||
<div class="flink-list-item">
|
<div class="flink-list-item">
|
||||||
<a href="https://www.youtube.com/" title="Youtube" target="_blank">
|
<a href="https://www.youtube.com/" title="Youtube" target="_blank">
|
||||||
<div class="flink-item-icon">
|
<div class="flink-item-icon">
|
||||||
<img class="no-lightbox" src="https://i.loli.net/2020/05/14/9ZkGg8v3azHJfM1.png" onerror='this.onerror=null;this.src="/img/friend_404.gif"' alt="Youtube" />
|
<img class="no-lightbox" src="https://i.loli.net/2020/05/14/9ZkGg8v3azHJfM1.png" onerror='this.onerror=null;this.src="/img/friend_404.gif"' alt="Youtube" />
|
||||||
</div>
|
</div>
|
||||||
<div class="flink-item-name">Youtube</div>
|
<div class="flink-item-name">Youtube</div>
|
||||||
<div class="flink-item-desc" title="視頻網站">視頻網站</div>
|
<div class="flink-item-desc" title="视频网站">视频网站</div>
|
||||||
</a>
|
</a>
|
||||||
</div>
|
</div>
|
||||||
<div class="flink-list-item">
|
<div class="flink-list-item">
|
||||||
@@ -178,7 +178,7 @@
|
|||||||
<img class="no-lightbox" src="https://i.loli.net/2020/05/14/TLJBum386vcnI1P.png" onerror='this.onerror=null;this.src="/img/friend_404.gif"' alt="Weibo" />
|
<img class="no-lightbox" src="https://i.loli.net/2020/05/14/TLJBum386vcnI1P.png" onerror='this.onerror=null;this.src="/img/friend_404.gif"' alt="Weibo" />
|
||||||
</div>
|
</div>
|
||||||
<div class="flink-item-name">Weibo</div>
|
<div class="flink-item-name">Weibo</div>
|
||||||
<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>
|
<div class="flink-item-desc" title="中国最大社交分享平台">中国最大社交分享平台</div>
|
||||||
</a>
|
</a>
|
||||||
</div>
|
</div>
|
||||||
<div class="flink-list-item">
|
<div class="flink-list-item">
|
||||||
@@ -189,14 +189,14 @@
|
|||||||
<div class="flink-item-name">Twitter</div>
|
<div class="flink-item-name">Twitter</div>
|
||||||
<div class="flink-item-desc" title="社交分享平台">社交分享平台</div>
|
<div class="flink-item-desc" title="社交分享平台">社交分享平台</div>
|
||||||
</a>
|
</a>
|
||||||
</div></div></div></div></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">
|
</div></div></div></div></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">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</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"><a class="thumbnail" href="/posts/8816.html" title="集成学习"><img src="/img/machinelearning/ensemble-learning.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="集成学习"/></a><div class="content"><a class="title" href="/posts/8816.html" title="集成学习">集成学习</a><time datetime="2025-01-25T07:12:08.000Z" title="发表于 2025-01-25 15:12:08">2025-01-25</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/posts/95.html" title="决策树算法"><img src="/img/machinelearning/decision-tree.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="决策树算法"/></a><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></div><div class="card-widget card-categories"><div class="item-headline">
|
||||||
<i class="fas fa-folder-open"></i>
|
<i class="fas fa-folder-open"></i>
|
||||||
<span>分类</span>
|
<span>分类</span>
|
||||||
|
|
||||||
</div>
|
</div>
|
||||||
<ul class="card-category-list" id="aside-cat-list">
|
<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>
|
<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">5</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/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"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
</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/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</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/%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/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</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/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</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">6</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">18</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="2025-10-16T03:29:44.082Z"><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"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></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"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
|
||||||
POWERMODE.shake = true;
|
POWERMODE.shake = true;
|
||||||
POWERMODE.mobile = false;
|
POWERMODE.mobile = false;
|
||||||
document.body.addEventListener('input', POWERMODE);
|
document.body.addEventListener('input', POWERMODE);
|
||||||
|
|||||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
172
public/posts/12462.html
Normal file
172
public/posts/12462.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
222
public/posts/52662.html
Normal file
222
public/posts/52662.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
228
public/posts/60504.html
Normal file
228
public/posts/60504.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
167
public/posts/8816.html
Normal file
167
public/posts/8816.html
Normal file
File diff suppressed because one or more lines are too long
193
public/posts/95.html
Normal file
193
public/posts/95.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
158
public/tags/KNN/index.html
Normal file
158
public/tags/KNN/index.html
Normal file
File diff suppressed because one or more lines are too long
158
public/tags/decisiontree/index.html
Normal file
158
public/tags/decisiontree/index.html
Normal file
File diff suppressed because one or more lines are too long
158
public/tags/ensemble-learning/index.html
Normal file
158
public/tags/ensemble-learning/index.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
158
public/tags/linear-regression/index.html
Normal file
158
public/tags/linear-regression/index.html
Normal file
File diff suppressed because one or more lines are too long
158
public/tags/logistic-regression/index.html
Normal file
158
public/tags/logistic-regression/index.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,22 +1,22 @@
|
|||||||
- class_name: 友情鏈接
|
- class_name: 友情链接
|
||||||
class_desc: 那些人，那些事
|
class_desc: 那些人，那些事
|
||||||
link_list:
|
link_list:
|
||||||
- name: Hexo
|
- name: Hexo
|
||||||
link: https://hexo.io/zh-tw/
|
link: https://hexo.io/zh-cn/
|
||||||
avatar: https://d33wubrfki0l68.cloudfront.net/6657ba50e702d84afb32fe846bed54fba1a77add/827ae/logo.svg
|
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_name: 网站
|
||||||
class_desc: 值得推薦的網站
|
class_desc: 值得推荐的网站
|
||||||
link_list:
|
link_list:
|
||||||
- name: Youtube
|
- name: Youtube
|
||||||
link: https://www.youtube.com/
|
link: https://www.youtube.com/
|
||||||
avatar: https://i.loli.net/2020/05/14/9ZkGg8v3azHJfM1.png
|
avatar: https://i.loli.net/2020/05/14/9ZkGg8v3azHJfM1.png
|
||||||
descr: 視頻網站
|
descr: 视频网站
|
||||||
- name: Weibo
|
- name: Weibo
|
||||||
link: https://www.weibo.com/
|
link: https://www.weibo.com/
|
||||||
avatar: https://i.loli.net/2020/05/14/TLJBum386vcnI1P.png
|
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
|
- name: Twitter
|
||||||
link: https://twitter.com/
|
link: https://twitter.com/
|
||||||
avatar: https://i.loli.net/2020/05/14/5VyHPQqR6LWF39a.png
|
avatar: https://i.loli.net/2020/05/14/5VyHPQqR6LWF39a.png
|
||||||
|
|||||||
81
source/_posts/language/C.md
Normal file
81
source/_posts/language/C.md
Normal file
@@ -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);
|
||||||
|
```
|
||||||
@@ -4,6 +4,8 @@ tags: decisiontree
|
|||||||
categories: machinelearning
|
categories: machinelearning
|
||||||
abbrlink: 95
|
abbrlink: 95
|
||||||
date: 2025-01-24 12:39:59
|
date: 2025-01-24 12:39:59
|
||||||
|
cover: /img/machinelearning/decision-tree.png
|
||||||
|
top_img: /img/site01.jpg
|
||||||
---
|
---
|
||||||
|
|
||||||
### C4.5
|
### C4.5
|
||||||
@@ -169,6 +171,8 @@ 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/)，这个网站可以将`tree.dot`文件的内容生成对应的可视化树
|
||||||
|
|
||||||
|
|
||||||
|
#### 回归决策树与线性回归的对比
|
||||||
```python
|
```python
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
|
|||||||
129
source/_posts/machinelearning/ensemblelearning.md
Normal file
129
source/_posts/machinelearning/ensemblelearning.md
Normal file
@@ -0,0 +1,129 @@
|
|||||||
|
---
|
||||||
|
title: 集成学习
|
||||||
|
tags: ensemble-learning
|
||||||
|
categories: machinelearning
|
||||||
|
abbrlink: 8816
|
||||||
|
date: 2025-01-25 15:12:08
|
||||||
|
cover: /img/machinelearning/ensemble-learning.png
|
||||||
|
top_img: /img/site01.jpg
|
||||||
|
---
|
||||||
|
|
||||||
|
### Bagging
|
||||||
|
|
||||||
|
### 随机森林
|
||||||
|
> `Random-Forest` 就是`Bagging + Decisiontree`
|
||||||
|
```python
|
||||||
|
import seaborn as sns
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
from sklearn.model_selection import train_test_split,GridSearchCV
|
||||||
|
from sklearn.feature_extraction import DictVectorizer
|
||||||
|
from sklearn.ensemble import RandomForestClassifier
|
||||||
|
# 1.获取数据集 - 加载 Titanic 数据集
|
||||||
|
titanic = sns.load_dataset('titanic')
|
||||||
|
missing_age_count = titanic['age'].isna().sum()
|
||||||
|
# print(f"缺失的 age 数量: {missing_age_count}")
|
||||||
|
# 2. 数据基本处理
|
||||||
|
# 2.1 确认特征值、目标值
|
||||||
|
X = titanic[['pclass','age','sex']]
|
||||||
|
y = titanic['survived']
|
||||||
|
# 2.2 缺失值处理
|
||||||
|
X.loc[:, 'age'] = X['age'].fillna(value=X['age'].mean()) # 使用 .loc 进行修改
|
||||||
|
# 2.3 划分数据集
|
||||||
|
X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=22)
|
||||||
|
# 3. 特征工程（字典特征提取）
|
||||||
|
X_train = X_train.to_dict(orient="records")
|
||||||
|
X_test= X_test.to_dict(orient="records")
|
||||||
|
transfer = DictVectorizer()
|
||||||
|
X_train = transfer.fit_transform(X_train)
|
||||||
|
X_test = transfer.transform(X_test)
|
||||||
|
# 4. 机器学习 随机森林
|
||||||
|
rf = RandomForestClassifier()
|
||||||
|
gc = GridSearchCV(estimator=rf ,param_grid={"n_estimators":[100,120,300],"max_depth":[3,7,11]},cv=3)
|
||||||
|
gc.fit(X_train,y_train)
|
||||||
|
y_pred = gc.predict(X_test)
|
||||||
|
print(f"模型的测试集的预测值：{y_pred}")
|
||||||
|
ret = gc.score(X_test,y_test)
|
||||||
|
print(f"最佳模型在测试集上的评分:{ret}")
|
||||||
|
print(f"最佳模型的参数：{gc.best_estimator_}")
|
||||||
|
print(f"最佳模型在训练集上的评分：{gc.best_score_}")
|
||||||
|
print(X_test.toarray())
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
### ott案例
|
||||||
|
```python
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from imblearn.under_sampling import RandomUnderSampler
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.preprocessing import LabelEncoder
|
||||||
|
from sklearn.ensemble import RandomForestClassifier
|
||||||
|
from sklearn.metrics import log_loss
|
||||||
|
from sklearn.preprocessing import OneHotEncoder
|
||||||
|
# 1. 获取数据集
|
||||||
|
data = pd.read_csv('./data/train.csv')
|
||||||
|
# 查看目标值分类
|
||||||
|
import seaborn as sns
|
||||||
|
sns.countplot(data=data, x='target', hue='target', palette="Set2", legend=False) # 使用 hue='target' 替代 palette
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
# 2. 数据集的基本处理
|
||||||
|
# 2.1 确定特征值、目标值
|
||||||
|
x = data.drop(["id", "target"], axis=1)
|
||||||
|
y = data['target']
|
||||||
|
|
||||||
|
# 2.2 使用随机欠采样进行平衡
|
||||||
|
undersampler = RandomUnderSampler(sampling_strategy='auto', random_state=0)
|
||||||
|
x_resampled, y_resampled = undersampler.fit_resample(x, y)
|
||||||
|
|
||||||
|
# 查看欠采样后的类别分布
|
||||||
|
# print(f"欠采样后训练集中的类别分布：\n{y_train_resampled.value_counts()}")
|
||||||
|
|
||||||
|
# 2.3. 将标签转换为数字
|
||||||
|
le = LabelEncoder()
|
||||||
|
y_resampled = le.fit_transform(y_resampled)
|
||||||
|
|
||||||
|
# 2.4. 划分训练集和测试集
|
||||||
|
x_train, x_test, y_train, y_test = train_test_split(x_resampled, y_resampled, test_size=0.2)
|
||||||
|
|
||||||
|
# 3. 机器学习
|
||||||
|
rf = RandomForestClassifier(oob_score = True)
|
||||||
|
rf.fit(x_train,y_train)
|
||||||
|
y_pred = rf.predict(x_test)
|
||||||
|
print(f"预测值:{y_pred}")
|
||||||
|
print(f"评分：{rf.score(x_test,y_test)}")
|
||||||
|
|
||||||
|
# # 4. 模型评估 （解决二分类预测问题）
|
||||||
|
# import numpy as np
|
||||||
|
# from sklearn.metrics import log_loss
|
||||||
|
# # 假设 y_pred_prob 是通过 predict_proba 得到的预测概率
|
||||||
|
# # 对预测概率进行裁剪，将其限制在 [eps, 1-eps] 范围内
|
||||||
|
# eps = 1e-15 # 设置一个小的eps值，避免极端值
|
||||||
|
# y_pred_prob = rf.predict_proba(x_test)
|
||||||
|
# y_pred_prob = np.clip(y_pred_prob, eps, 1 - eps)
|
||||||
|
|
||||||
|
# # 计算 log_loss
|
||||||
|
# loss = log_loss(y_test, y_pred_prob, normalize=True)
|
||||||
|
# print(f"Log Loss: {loss}")
|
||||||
|
|
||||||
|
# 4. 模型评估 （解决多分类预测问题）
|
||||||
|
|
||||||
|
# 获取预测的概率
|
||||||
|
y_pred_prob = rf.predict_proba(x_test)
|
||||||
|
|
||||||
|
# 使用 OneHotEncoder 对 y_test 进行 One-Hot 编码
|
||||||
|
encoder = OneHotEncoder(sparse_output=False) # 确保返回的是密集矩阵
|
||||||
|
y_test_one_hot = encoder.fit_transform(y_test.reshape(-1, 1))
|
||||||
|
|
||||||
|
# 对预测概率进行裁剪，将其限制在 [eps, 1-eps] 范围内
|
||||||
|
eps = 1e-15
|
||||||
|
y_pred_prob = np.clip(y_pred_prob, eps, 1 - eps)
|
||||||
|
|
||||||
|
# 计算 log_loss
|
||||||
|
loss = log_loss(y_test_one_hot, y_pred_prob, normalize=True)
|
||||||
|
print(f"Log Loss: {loss}")
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
@@ -1,6 +1,7 @@
|
|||||||
---
|
---
|
||||||
title: k近邻算法（K-Nearest Neighbors）KNN
|
title: k近邻算法（K-Nearest Neighbors）KNN
|
||||||
tags: machinelearning
|
tags: KNN
|
||||||
|
categories: machinelearning
|
||||||
abbrlink: 29139
|
abbrlink: 29139
|
||||||
mathjax: true
|
mathjax: true
|
||||||
date: 2025-01-13 17:20:59
|
date: 2025-01-13 17:20:59
|
||||||
|
|||||||
200
source/_posts/machinelearning/linearreression.md
Normal file
200
source/_posts/machinelearning/linearreression.md
Normal file
@@ -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）**：
|
||||||
|
$$J(\theta) = \frac{1}{2N} \sum_{i=1}^{N} (y_i - f_\theta(x_i))^2$$
|
||||||
|
- N 是样本的数量。
|
||||||
|
- $y_i$ 是第 i 个样本的真实值。
|
||||||
|
- $f_\theta(x_i)$ 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()
|
||||||
|
```
|
||||||
173
source/_posts/machinelearning/logisticregression.md
Normal file
173
source/_posts/machinelearning/logisticregression.md
Normal file
@@ -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()
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
@@ -43,7 +43,13 @@ date: 2024-08-07 10:06:08
|
|||||||
- `赔钱机场`
|
- `赔钱机场`
|
||||||
|
|
||||||
|
|
||||||
`赔钱机场`的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：
|
可以看到
|
||||||
|
- `18元/年`,每月100GB的可用额度，允许最多10个设备同时在线，下个月重置流量额度
|
||||||
|
- `34.99元/年`，每月有500GB的可用额度，允许最多15个设备同时在线，下个月重置流量额度
|
||||||
|
- `68.99元/年`，每个月1000GB的可用额度，允许最多20个设备同时在线，下个月重置流量额度
|
||||||
|
- 其余可以自行查看
|
||||||
|
|
||||||
|
`赔钱机场`的订阅共有5种方案(按周期付费)，这里我仅显示自己正在使用的，个人认为十分优惠：
|
||||||
- `34.99元/年`,每月500GB的可用额度，根据我观察和使用，这个订阅方案比`一元机场`的性价比更高，且流量使用额度也不用担心
|
- `34.99元/年`,每月500GB的可用额度，根据我观察和使用，这个订阅方案比`一元机场`的性价比更高，且流量使用额度也不用担心
|
||||||
|
|
||||||
### 如何订阅？
|
### 如何订阅？
|
||||||
|
|||||||
BIN
source/img/language/c-env-conf.png
Normal file
BIN
source/img/language/c-env-conf.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 126 KiB |
BIN
source/img/machinelearning/decision-tree.png
Normal file
BIN
source/img/machinelearning/decision-tree.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 537 KiB |
BIN
source/img/machinelearning/ensemble-learning.png
Normal file
BIN
source/img/machinelearning/ensemble-learning.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 613 KiB |
BIN
source/img/machinelearning/fitting.png
Normal file
BIN
source/img/machinelearning/fitting.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 100 KiB |
BIN
source/img/machinelearning/linear.png
Normal file
BIN
source/img/machinelearning/linear.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 123 KiB |
BIN
source/img/machinelearning/ott.png
Normal file
BIN
source/img/machinelearning/ott.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 53 KiB |
BIN
source/img/machinelearning/over_random_sampling.png
Normal file
BIN
source/img/machinelearning/over_random_sampling.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 29 KiB |
BIN
source/img/machinelearning/over_smote_sampling.png
Normal file
BIN
source/img/machinelearning/over_smote_sampling.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 39 KiB |
BIN
source/img/machinelearning/random-forest.png
Normal file
BIN
source/img/machinelearning/random-forest.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 116 KiB |
BIN
source/img/machinelearning/under_sampling.png
Normal file
BIN
source/img/machinelearning/under_sampling.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 36 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 266 KiB After Width: | Height: | Size: 770 KiB |
Reference in New Issue
Block a user