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1112 lines
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1112 lines
30 KiB
6 years ago
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<!DOCTYPE HTML>
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<html lang="" >
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<head>
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<meta charset="UTF-8">
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<meta content="text/html; charset=utf-8" http-equiv="Content-Type">
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<title>7.5:实战案例 · GitBook</title>
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<meta name="apple-mobile-web-app-status-bar-style" content="black">
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<link rel="next" href="../Chapter8/" />
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<link rel="prev" href="动手实现k-均值.html" />
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</head>
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<body>
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<div class="book-summary">
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<div id="book-search-input" role="search">
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<input type="text" placeholder="Type to search" />
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<nav role="navigation">
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<ul class="summary">
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<li class="chapter " data-level="1.1" data-path="../">
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<a href="../">
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前言
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</a>
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</li>
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<li class="chapter " data-level="1.2" data-path="../Chapter1/">
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<a href="../Chapter1/">
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第一章 绪论
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.2.1" data-path="../Chapter1/为什么要数据挖掘.html">
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<a href="../Chapter1/为什么要数据挖掘.html">
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1.1:为什么要数据挖掘
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</a>
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</li>
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<li class="chapter " data-level="1.2.2" data-path="../Chapter1/什么是数据挖掘.html">
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<a href="../Chapter1/什么是数据挖掘.html">
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1.2: 什么是数据挖掘
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</a>
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</li>
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<li class="chapter " data-level="1.2.3" data-path="../Chapter1/数据挖掘主要任务.html">
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<a href="../Chapter1/数据挖掘主要任务.html">
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1.3:数据挖掘主要任务
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.3" data-path="../Chapter2/">
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<a href="../Chapter2/">
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第二章 数据探索
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.3.1" data-path="../Chapter2/数据与属性.html">
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<a href="../Chapter2/数据与属性.html">
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2.1:数据与属性
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</a>
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</li>
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<li class="chapter " data-level="1.3.2" data-path="../Chapter2/数据的基本统计指标.html">
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<a href="../Chapter2/数据的基本统计指标.html">
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2.2:数据的基本统计指标
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</a>
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</li>
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<li class="chapter " data-level="1.3.3" data-path="../Chapter2/数据可视化.html">
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<a href="../Chapter2/数据可视化.html">
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2.3:数据可视化
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</a>
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</li>
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<li class="chapter " data-level="1.3.4" data-path="../Chapter2/相似性度量.html">
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<a href="../Chapter2/相似性度量.html">
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2.4:相似性度量
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.4" data-path="../Chapter3/">
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<a href="../Chapter3/">
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第三章 数据预处理
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.4.1" data-path="../Chapter3/为什么要数据预处理.html">
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<a href="../Chapter3/为什么要数据预处理.html">
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3.1:为什么要数据预处理
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</a>
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</li>
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<li class="chapter " data-level="1.4.2" data-path="../Chapter3/标准化.html">
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<a href="../Chapter3/标准化.html">
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3.2:标准化
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</a>
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</li>
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<li class="chapter " data-level="1.4.3" data-path="../Chapter3/非线性变换.html">
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<a href="../Chapter3/非线性变换.html">
|
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3.3:非线性变换
|
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</a>
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</li>
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<li class="chapter " data-level="1.4.4" data-path="../Chapter3/归一化.html">
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<a href="../Chapter3/归一化.html">
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3.4:归一化
|
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</a>
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</li>
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<li class="chapter " data-level="1.4.5" data-path="../Chapter3/离散值编码.html">
|
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<a href="../Chapter3/离散值编码.html">
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3.5:离散值编码
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</a>
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</li>
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<li class="chapter " data-level="1.4.6" data-path="../Chapter3/生成多项式特征.html">
|
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<a href="../Chapter3/生成多项式特征.html">
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3.6:生成多项式特征
|
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</a>
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</li>
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<li class="chapter " data-level="1.4.7" data-path="../Chapter3/估算缺失值.html">
|
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<a href="../Chapter3/估算缺失值.html">
|
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3.7:估算缺失值
|
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.5" data-path="../Chapter4/">
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<a href="../Chapter4/">
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第四章 k-近邻
|
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</a>
|
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<ul class="articles">
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<li class="chapter " data-level="1.5.1" data-path="../Chapter4/k-近邻算法思想.html">
|
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<a href="../Chapter4/k-近邻算法思想.html">
|
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4.1:k-近邻算法思想
|
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</a>
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</li>
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<li class="chapter " data-level="1.5.2" data-path="../Chapter4/k-近邻算法原理.html">
|
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<a href="../Chapter4/k-近邻算法原理.html">
|
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4.2:k-近邻算法原理
|
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</a>
|
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</li>
|
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<li class="chapter " data-level="1.5.3" data-path="../Chapter4/k-近邻算法流程.html">
|
||
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<a href="../Chapter4/k-近邻算法流程.html">
|
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4.3:k-近邻算法流程
|
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</a>
|
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</li>
|
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<li class="chapter " data-level="1.5.4" data-path="../Chapter4/动手实现k-近邻.html">
|
||
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|
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<a href="../Chapter4/动手实现k-近邻.html">
|
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4.4:动手实现k-近邻
|
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</a>
|
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</li>
|
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<li class="chapter " data-level="1.5.5" data-path="../Chapter4/实战案例.html">
|
||
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<a href="../Chapter4/实战案例.html">
|
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4.5:实战案例
|
||
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</a>
|
||
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</li>
|
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</ul>
|
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</li>
|
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<li class="chapter " data-level="1.6" data-path="../Chapter5/">
|
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<a href="../Chapter5/">
|
||
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第五章 线性回归
|
||
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</a>
|
||
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<ul class="articles">
|
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<li class="chapter " data-level="1.6.1" data-path="../Chapter5/线性回归算法思想.html">
|
||
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<a href="../Chapter5/线性回归算法思想.html">
|
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5.1:线性回归算法思想
|
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</a>
|
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</li>
|
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<li class="chapter " data-level="1.6.2" data-path="../Chapter5/线性回归算法原理.html">
|
||
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<a href="../Chapter5/线性回归算法原理.html">
|
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5.2:线性回归算法原理
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.6.3" data-path="../Chapter5/线性回归算法流程.html">
|
||
|
|
|
||
|
|
<a href="../Chapter5/线性回归算法流程.html">
|
||
|
|
|
||
|
|
|
||
|
|
5.3:线性回归算法流程
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.6.4" data-path="../Chapter5/动手实现线性回归.html">
|
||
|
|
|
||
|
|
<a href="../Chapter5/动手实现线性回归.html">
|
||
|
|
|
||
|
|
|
||
|
|
5.4:动手实现线性回归
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.6.5" data-path="../Chapter5/实战案例.html">
|
||
|
|
|
||
|
|
<a href="../Chapter5/实战案例.html">
|
||
|
|
|
||
|
|
|
||
|
|
5.5:实战案例
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.7" data-path="../Chapter6/">
|
||
|
|
|
||
|
|
<a href="../Chapter6/">
|
||
|
|
|
||
|
|
|
||
|
|
第六章 决策树
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.7.1" data-path="../Chapter6/决策树算法思想.html">
|
||
|
|
|
||
|
|
<a href="../Chapter6/决策树算法思想.html">
|
||
|
|
|
||
|
|
|
||
|
|
6.1:决策树算法思想
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.7.2" data-path="../Chapter6/决策树算法原理.html">
|
||
|
|
|
||
|
|
<a href="../Chapter6/决策树算法原理.html">
|
||
|
|
|
||
|
|
|
||
|
|
6.2:决策树算法原理
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.7.3" data-path="../Chapter6/决策树算法流程.html">
|
||
|
|
|
||
|
|
<a href="../Chapter6/决策树算法流程.html">
|
||
|
|
|
||
|
|
|
||
|
|
6.3:决策树算法流程
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.7.4" data-path="../Chapter6/动手实现决策树.html">
|
||
|
|
|
||
|
|
<a href="../Chapter6/动手实现决策树.html">
|
||
|
|
|
||
|
|
|
||
|
|
6.4:动手实现决策树
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.7.5" data-path="../Chapter6/实战案例.html">
|
||
|
|
|
||
|
|
<a href="../Chapter6/实战案例.html">
|
||
|
|
|
||
|
|
|
||
|
|
6.5:实战案例
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.8" data-path="./">
|
||
|
|
|
||
|
|
<a href="./">
|
||
|
|
|
||
|
|
|
||
|
|
第七章 k-均值
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.8.1" data-path="k-均值算法思想.html">
|
||
|
|
|
||
|
|
<a href="k-均值算法思想.html">
|
||
|
|
|
||
|
|
|
||
|
|
7.1:k-均值算法思想
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.8.2" data-path="k-均值算法原理.html">
|
||
|
|
|
||
|
|
<a href="k-均值算法原理.html">
|
||
|
|
|
||
|
|
|
||
|
|
7.2:k-均值算法原理
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.8.3" data-path="k-均值算法流程.html">
|
||
|
|
|
||
|
|
<a href="k-均值算法流程.html">
|
||
|
|
|
||
|
|
|
||
|
|
7.3:k-均值算法流程
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.8.4" data-path="动手实现k-均值.html">
|
||
|
|
|
||
|
|
<a href="动手实现k-均值.html">
|
||
|
|
|
||
|
|
|
||
|
|
7.4:动手实现k-均值
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter active" data-level="1.8.5" data-path="实战案例.html">
|
||
|
|
|
||
|
|
<a href="实战案例.html">
|
||
|
|
|
||
|
|
|
||
|
|
7.5:实战案例
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.9" data-path="../Chapter8/">
|
||
|
|
|
||
|
|
<a href="../Chapter8/">
|
||
|
|
|
||
|
|
|
||
|
|
第八章 Apriori
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.9.1" data-path="../Chapter8/Apriori算法思想.html">
|
||
|
|
|
||
|
|
<a href="../Chapter8/Apriori算法思想.html">
|
||
|
|
|
||
|
|
|
||
|
|
8.1:Apriori算法思想
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.9.2" data-path="../Chapter8/Apriori算法原理.html">
|
||
|
|
|
||
|
|
<a href="../Chapter8/Apriori算法原理.html">
|
||
|
|
|
||
|
|
|
||
|
|
8.2:Apriori算法原理
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.9.3" data-path="../Chapter8/Apriori算法流程.html">
|
||
|
|
|
||
|
|
<a href="../Chapter8/Apriori算法流程.html">
|
||
|
|
|
||
|
|
|
||
|
|
8.3:Apriori算法流程
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.9.4" data-path="../Chapter8/动手实现Apriori.html">
|
||
|
|
|
||
|
|
<a href="../Chapter8/动手实现Apriori.html">
|
||
|
|
|
||
|
|
|
||
|
|
8.4:动手实现Apriori
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.9.5" data-path="../Chapter8/实战案例.html">
|
||
|
|
|
||
|
|
<a href="../Chapter8/实战案例.html">
|
||
|
|
|
||
|
|
|
||
|
|
8.5:实战案例
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.10" data-path="../Chapter9/">
|
||
|
|
|
||
|
|
<a href="../Chapter9/">
|
||
|
|
|
||
|
|
|
||
|
|
第九章 PageRank
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.10.1" data-path="../Chapter9/PageRank算法思想.html">
|
||
|
|
|
||
|
|
<a href="../Chapter9/PageRank算法思想.html">
|
||
|
|
|
||
|
|
|
||
|
|
9.1:PageRank算法思想
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.10.2" data-path="../Chapter9/PageRank算法原理.html">
|
||
|
|
|
||
|
|
<a href="../Chapter9/PageRank算法原理.html">
|
||
|
|
|
||
|
|
|
||
|
|
9.2:PageRank算法原理
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.10.3" data-path="../Chapter9/PageRank算法流程.html">
|
||
|
|
|
||
|
|
<a href="../Chapter9/PageRank算法流程.html">
|
||
|
|
|
||
|
|
|
||
|
|
9.3:PageRank算法流程
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.10.4" data-path="../Chapter9/动手实现PageRank.html">
|
||
|
|
|
||
|
|
<a href="../Chapter9/动手实现PageRank.html">
|
||
|
|
|
||
|
|
|
||
|
|
9.4:动手实现PageRank
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.10.5" data-path="../Chapter9/实战案例.html">
|
||
|
|
|
||
|
|
<a href="../Chapter9/实战案例.html">
|
||
|
|
|
||
|
|
|
||
|
|
9.5:实战案例
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.11" data-path="../Chapter10/">
|
||
|
|
|
||
|
|
<a href="../Chapter10/">
|
||
|
|
|
||
|
|
|
||
|
|
第十章 推荐系统
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.11.1" data-path="../Chapter10/推荐系统概述.html">
|
||
|
|
|
||
|
|
<a href="../Chapter10/推荐系统概述.html">
|
||
|
|
|
||
|
|
|
||
|
|
10.1:推荐系统概述
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.11.2" data-path="../Chapter10/基于矩阵分解的协同过滤算法思想.html">
|
||
|
|
|
||
|
|
<a href="../Chapter10/基于矩阵分解的协同过滤算法思想.html">
|
||
|
|
|
||
|
|
|
||
|
|
10.2:基于矩阵分解的协同过滤算法思想
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.11.3" data-path="../Chapter10/基于矩阵分解的协同过滤算法原理.html">
|
||
|
|
|
||
|
|
<a href="../Chapter10/基于矩阵分解的协同过滤算法原理.html">
|
||
|
|
|
||
|
|
|
||
|
|
10.3:基于矩阵分解的协同过滤算法原理
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.11.4" data-path="../Chapter10/基于矩阵分解的协同过滤算法流程.html">
|
||
|
|
|
||
|
|
<a href="../Chapter10/基于矩阵分解的协同过滤算法流程.html">
|
||
|
|
|
||
|
|
|
||
|
|
10.4:基于矩阵分解的协同过滤算法流程
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.11.5" data-path="../Chapter10/动手实现基于矩阵分解的协同过滤.html">
|
||
|
|
|
||
|
|
<a href="../Chapter10/动手实现基于矩阵分解的协同过滤.html">
|
||
|
|
|
||
|
|
|
||
|
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10.5:动手实现基于矩阵分解的协同过滤
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10.6:实战案例
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<a href=".." >7.5:实战案例</a>
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<h1 id="75实战案例">7.5:实战案例</h1>
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<h3 id="鸢尾花数据">鸢尾花数据</h3>
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<p>本次我们使用的仍然是鸢尾花数据，不过为了能够进行可视化我们只使用数据中的两个特征：</p>
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<pre><code class="lang-python"><span class="hljs-keyword">from</span> sklearn.datasets <span class="hljs-keyword">import</span> load_iris
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iris = load_iris()
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x,y = iris.data,iris.target
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x = x[:,<span class="hljs-number">2</span>:]
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</code></pre>
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<p>可视化数据分布：</p>
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<pre><code class="lang-python"><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
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plt.scatter(x[:,<span class="hljs-number">0</span>],x[:,<span class="hljs-number">1</span>])
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plt.show()
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</code></pre>
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<p>可视化结果：</p>
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<p><img src="k-means.jpg" alt="k-means"></p>
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<p>我们可以先根据数据的真实标签查看数据类别情况：</p>
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<pre><code class="lang-python"><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
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plt.scatter(x[:,<span class="hljs-number">0</span>],x[:,<span class="hljs-number">1</span>],c=y)
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plt.show()
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</code></pre>
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<p>效果如下：</p>
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<p><img src="kmeans1.jpg" alt="kmeans1"></p>
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<h3 id="进行聚类">进行聚类</h3>
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<p>最后，使用我们实现的<code>k-means</code>方法对数据进行聚类并查看聚类效果：</p>
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<pre><code class="lang-python">predict = predict(<span class="hljs-number">3</span>,x,<span class="hljs-number">500</span>,<span class="hljs-number">0.0001</span>)
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plt.scatter(x[:,<span class="hljs-number">0</span>],x[:,<span class="hljs-number">1</span>],c=predict)
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plt.show()
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</code></pre>
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<p><img src="kmeans2.jpg" alt="kmeans2"></p>
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<p>可以发现，使用实现的方法进行聚类的结果与真实情况非常吻合。</p>
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