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简介
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机器学习概述
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常见机器学习算法
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近朱者赤近墨者黑-kNN
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最简单的回归算法-线性回归
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使用回归的思想进行分类-逻辑回归
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最接近人类思维的算法-决策树
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群众的力量是伟大的-随机森林
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物以类聚人以群分-kMeans
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<a href="../AGNES.html">
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以距离为尺-AGNES
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</a>
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模型评估指标
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分类性能评估指标
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回归性能评估指标
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聚类性能评估指标
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使用sklearn进行机器学习
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</a>
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<li class="chapter " data-level="1.6" >
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<span>
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综合实战案例
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<ul class="articles">
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<span>
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泰坦尼克生还预测
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简介
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探索性数据分析(EDA)
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</a>
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特征工程
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</a>
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<a href="fit and predict.html">
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构建模型进行预测
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</a>
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调参
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</a>
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<li class="chapter " data-level="1.6.2" >
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<span>
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使用强化学习玩乒乓球游戏
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</span>
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<ul class="articles">
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<a href="../pingpong/what is reinforce learning.html">
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什么是强化学习
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</a>
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<li class="chapter " data-level="1.6.2.2" data-path="../pingpong/Policy Gradient.html">
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<a href="../pingpong/Policy Gradient.html">
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Policy Gradient原理
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</a>
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<li class="chapter " data-level="1.6.2.3" data-path="../pingpong/coding.html">
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<a href="../pingpong/coding.html">
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使用Policy Gradient玩乒乓球游戏
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</a>
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实训推荐
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<!-- Title -->
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<h1>
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<a href=".." >简介</a>
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</h1>
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<section class="normal markdown-section">
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<h1 id="写在前面的话">写在前面的话</h1>
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<p>怎样处理数据，使用什么样的机器学习模型并没有所谓的正确答案。这篇文章只是抛砖引玉，若您是刚刚接触数据科学，我相信这一篇不错的指引；若您已经是老手，我相信文中的一些技巧您肯定也用过，可以温故而知新；所以希望这篇文章对您或多或少的有所帮助。</p>
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<h1 id="泰坦尼克生还问题简介">泰坦尼克生还问题简介</h1>
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<p>泰坦尼克号的沉船事件是是历史上最臭名昭著的沉船事件之一。1912年4月15日，泰坦尼克在航线中与冰山相撞，2224 名乘客中有 1502 名乘客丧生。</p>
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<p>泰坦尼克号数据集是目标是给出一个模型来预测某位泰坦尼克号的乘客在沉船事件中是生还是死。而且该数据集是一个非常好的数据集，能够让您快速的开始数据科学之旅。</p>
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<p><img src="../img/59.jpg" alt=""></p>
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