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1157 lines
34 KiB
1157 lines
34 KiB
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<body>
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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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<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
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</a>
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</li>
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<li class="chapter " data-level="1.6.3" data-path="../Chapter5/线性回归算法流程.html">
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<a href="../Chapter5/线性回归算法流程.html">
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5.3:线性回归算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.6.4" data-path="../Chapter5/动手实现线性回归.html">
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<a href="../Chapter5/动手实现线性回归.html">
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5.4:动手实现线性回归
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</a>
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</li>
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<li class="chapter " data-level="1.6.5" data-path="../Chapter5/实战案例.html">
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<a href="../Chapter5/实战案例.html">
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5.5: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="1.7" data-path="./">
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<a href="./">
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第六章 决策树
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.7.1" data-path="决策树算法思想.html">
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<a href="决策树算法思想.html">
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6.1:决策树算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.7.2" data-path="决策树算法原理.html">
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<a href="决策树算法原理.html">
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6.2:决策树算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.7.3" data-path="决策树算法流程.html">
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<a href="决策树算法流程.html">
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6.3:决策树算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.7.4" data-path="动手实现决策树.html">
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<a href="动手实现决策树.html">
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6.4:动手实现决策树
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</a>
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</li>
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<li class="chapter active" data-level="1.7.5" data-path="实战案例.html">
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<a href="实战案例.html">
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6.5: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="1.8" data-path="../Chapter7/">
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<a href="../Chapter7/">
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第七章 k-均值
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.8.1" data-path="../Chapter7/k-均值算法思想.html">
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<a href="../Chapter7/k-均值算法思想.html">
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7.1:k-均值算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.8.2" data-path="../Chapter7/k-均值算法原理.html">
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<a href="../Chapter7/k-均值算法原理.html">
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7.2:k-均值算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.8.3" data-path="../Chapter7/k-均值算法流程.html">
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<a href="../Chapter7/k-均值算法流程.html">
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7.3:k-均值算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.8.4" data-path="../Chapter7/动手实现k-均值.html">
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<a href="../Chapter7/动手实现k-均值.html">
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7.4:动手实现k-均值
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</a>
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</li>
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<li class="chapter " data-level="1.8.5" data-path="../Chapter7/实战案例.html">
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<a href="../Chapter7/实战案例.html">
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7.5: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="1.9" data-path="../Chapter8/">
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<a href="../Chapter8/">
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第八章 Apriori
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.9.1" data-path="../Chapter8/Apriori算法思想.html">
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<a href="../Chapter8/Apriori算法思想.html">
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8.1:Apriori算法思想
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</a>
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<li class="chapter " data-level="1.9.2" data-path="../Chapter8/Apriori算法原理.html">
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<a href="../Chapter8/Apriori算法原理.html">
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8.2:Apriori算法原理
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</a>
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<li class="chapter " data-level="1.9.3" data-path="../Chapter8/Apriori算法流程.html">
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<a href="../Chapter8/Apriori算法流程.html">
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8.3:Apriori算法流程
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</a>
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<li class="chapter " data-level="1.9.4" data-path="../Chapter8/动手实现Apriori.html">
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<a href="../Chapter8/动手实现Apriori.html">
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8.4:动手实现Apriori
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</a>
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<li class="chapter " data-level="1.9.5" data-path="../Chapter8/实战案例.html">
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<a href="../Chapter8/实战案例.html">
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8.5:实战案例
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</a>
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<li class="chapter " data-level="1.10" data-path="../Chapter9/">
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<a href="../Chapter9/">
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第九章 PageRank
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<ul class="articles">
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<li class="chapter " data-level="1.10.1" data-path="../Chapter9/PageRank算法思想.html">
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<a href="../Chapter9/PageRank算法思想.html">
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9.1:PageRank算法思想
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</a>
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<li class="chapter " data-level="1.10.2" data-path="../Chapter9/PageRank算法原理.html">
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<a href="../Chapter9/PageRank算法原理.html">
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9.2:PageRank算法原理
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<li class="chapter " data-level="1.10.3" data-path="../Chapter9/PageRank算法流程.html">
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<a href="../Chapter9/PageRank算法流程.html">
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9.3:PageRank算法流程
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<a href="../Chapter9/动手实现PageRank.html">
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9.4:动手实现PageRank
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</a>
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<li class="chapter " data-level="1.10.5" data-path="../Chapter9/实战案例.html">
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9.5:实战案例
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第十章 推荐系统
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<ul class="articles">
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<a href="../Chapter10/推荐系统概述.html">
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10.1:推荐系统概述
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</a>
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</li>
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<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">
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<a href="../Chapter10/基于矩阵分解的协同过滤算法思想.html">
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10.2:基于矩阵分解的协同过滤算法思想
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</a>
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</li>
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<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">
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<a href="../Chapter10/基于矩阵分解的协同过滤算法原理.html">
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10.3:基于矩阵分解的协同过滤算法原理
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</a>
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</li>
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<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">
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<a href="../Chapter10/基于矩阵分解的协同过滤算法流程.html">
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10.4:基于矩阵分解的协同过滤算法流程
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</a>
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</li>
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<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">
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<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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</a>
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<li class="chapter " data-level="1.11.6" data-path="../Chapter10/实战案例.html">
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10.6:实战案例
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<a href=".." >6.5:实战案例</a>
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<h1 id="65实战案例">6.5: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>鸢尾花数据集是一类多重变量分析的<strong>数据集</strong>，一共有<code>150</code>个样本，通过<strong>花萼长度</strong>，<strong>花萼宽度</strong>，<strong>花瓣长度</strong>，<strong>花瓣宽度</strong> <code>4</code>个特征预测鸢尾花卉属于（<code>Setosa</code>，<code>Versicolour</code>，<code>Virginica</code>）三个种类中的哪一类。</p>
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<p>数据集中部分<strong>数据</strong>如下所示：</p>
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<table>
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<thead>
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<tr>
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<th>花萼长度</th>
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<th>花萼宽度</th>
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<th>花瓣长度</th>
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<th>花瓣宽度</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>5.1</td>
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<td>3.5</td>
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<td>1.4</td>
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<td>0.2</td>
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</tr>
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<tr>
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<td>4.9</td>
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<td>3.2</td>
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<td>1.4</td>
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<td>0.2</td>
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</tr>
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<tr>
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<td>4.7</td>
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<td>3.1</td>
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<td>1.3</td>
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<td>0.2</td>
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</tr>
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</tbody>
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</table>
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<p>其中每一行代表一个鸢尾花样本各个属性的值。</p>
|
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<p>数据集中部分<strong>标签</strong>如下图所示：</p>
|
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<table>
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<thead>
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<tr>
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<th>标签</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>0</td>
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</tr>
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<tr>
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<td>1</td>
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</tr>
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<tr>
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<td>2</td>
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</tr>
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</tbody>
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</table>
|
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<p>标签中的值<code>0</code>,<code>1</code>,<code>2</code>分别代表鸢尾花三种不同的类别。</p>
|
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<p>我们可以直接使用<code>sklearn</code>直接对数据进行加载，代码如下：</p>
|
|
<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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<span class="hljs-comment">#加载鸢尾花数据集</span>
|
|
iris = load_iris()
|
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<span class="hljs-comment">#获取数据特征与标签</span>
|
|
x,y = iris.data.astype(int),iris.target
|
|
</code></pre>
|
|
<p>然后我们划分出训练集与测试集，训练集用来训练模型，测试集用来检测模型性能。代码如下：</p>
|
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<pre><code class="lang-python"><span class="hljs-keyword">from</span> sklearn.model_selection <span class="hljs-keyword">import</span> train_test_split
|
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<span class="hljs-comment">#划分训练集测试集，其中测试集样本数为整个数据集的20%</span>
|
|
train_feature,test_feature,train_label,test_label = train_test_split(x,y,test_size=<span class="hljs-number">0.2</span>,random_state=<span class="hljs-number">666</span>)
|
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</code></pre>
|
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<h3 id="进行分类">进行分类</h3>
|
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<p>然后我们再使用实现的决策树分类方法就可以对测试集数据进行分类：</p>
|
|
<pre><code class="lang-python">predict = dt_clf(train_feature,train_label,test_feature)
|
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predict
|
|
>>>array([<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">2</span>, <span class="hljs-number">2</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>,<span class="hljs-number">2</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">2</span>])
|
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</code></pre>
|
|
<p>再根据测试集标签，可以计算出正确率：</p>
|
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<pre><code class="lang-python">acc = np.mean(predict==test_label)
|
|
acc
|
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>>><span class="hljs-number">1.0</span>
|
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</code></pre>
|
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<p>可以看到，使用决策树对鸢尾花进行分类，正确率可以达到<code>100%</code></p>
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