You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
657 lines
25 KiB
657 lines
25 KiB
5 years ago
|
|
||
|
|
<!DOCTYPE HTML>
|
||
|
|
<html lang="" >
|
||
|
|
<head>
|
||
|
|
<meta charset="UTF-8">
|
||
|
|
<meta content="text/html; charset=utf-8" http-equiv="Content-Type">
|
||
|
|
<title>构建模型进行预测 · GitBook</title>
|
||
|
|
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
|
||
|
|
<meta name="description" content="">
|
||
|
|
<meta name="generator" content="GitBook 3.2.3">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<link rel="stylesheet" href="../gitbook/style.css">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-katex/katex.min.css">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-highlight/website.css">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-search/search.css">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-fontsettings/website.css">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<meta name="HandheldFriendly" content="true"/>
|
||
|
|
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no">
|
||
|
|
<meta name="apple-mobile-web-app-capable" content="yes">
|
||
|
|
<meta name="apple-mobile-web-app-status-bar-style" content="black">
|
||
|
|
<link rel="apple-touch-icon-precomposed" sizes="152x152" href="../gitbook/images/apple-touch-icon-precomposed-152.png">
|
||
|
|
<link rel="shortcut icon" href="../gitbook/images/favicon.ico" type="image/x-icon">
|
||
|
|
|
||
|
|
|
||
|
|
<link rel="next" href="tuning.html" />
|
||
|
|
|
||
|
|
|
||
|
|
<link rel="prev" href="feature engerning.html" />
|
||
|
|
|
||
|
|
|
||
|
|
</head>
|
||
|
|
<body>
|
||
|
|
|
||
|
|
<div class="book">
|
||
|
|
<div class="book-summary">
|
||
|
|
|
||
|
|
|
||
|
|
<div id="book-search-input" role="search">
|
||
|
|
<input type="text" placeholder="Type to search" />
|
||
|
|
</div>
|
||
|
|
|
||
|
|
|
||
|
|
<nav role="navigation">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="summary">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.1" data-path="../">
|
||
|
|
|
||
|
|
<a href="../">
|
||
|
|
|
||
|
|
|
||
|
|
简介
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.2" data-path="../machine_learning.html">
|
||
|
|
|
||
|
|
<a href="../machine_learning.html">
|
||
|
|
|
||
|
|
|
||
|
|
机器学习概述
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3" data-path="../algorithm.html">
|
||
|
|
|
||
|
|
<a href="../algorithm.html">
|
||
|
|
|
||
|
|
|
||
|
|
常见机器学习算法
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3.1" data-path="../kNN.html">
|
||
|
|
|
||
|
|
<a href="../kNN.html">
|
||
|
|
|
||
|
|
|
||
|
|
近朱者赤近墨者黑-kNN
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3.2" data-path="../linear_regression.html">
|
||
|
|
|
||
|
|
<a href="../linear_regression.html">
|
||
|
|
|
||
|
|
|
||
|
|
最简单的回归算法-线性回归
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3.3" data-path="../logistic_regression.html">
|
||
|
|
|
||
|
|
<a href="../logistic_regression.html">
|
||
|
|
|
||
|
|
|
||
|
|
使用回归的思想进行分类-逻辑回归
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3.4" data-path="../decision_tree.html">
|
||
|
|
|
||
|
|
<a href="../decision_tree.html">
|
||
|
|
|
||
|
|
|
||
|
|
最接近人类思维的算法-决策树
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3.5" data-path="../random_forest.html">
|
||
|
|
|
||
|
|
<a href="../random_forest.html">
|
||
|
|
|
||
|
|
|
||
|
|
群众的力量是伟大的-随机森林
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3.6" data-path="../kMeans.html">
|
||
|
|
|
||
|
|
<a href="../kMeans.html">
|
||
|
|
|
||
|
|
|
||
|
|
物以类聚人以群分-kMeans
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.3.7" data-path="../AGNES.html">
|
||
|
|
|
||
|
|
<a href="../AGNES.html">
|
||
|
|
|
||
|
|
|
||
|
|
以距离为尺-AGNES
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.4" data-path="../metrics.html">
|
||
|
|
|
||
|
|
<a href="../metrics.html">
|
||
|
|
|
||
|
|
|
||
|
|
模型评估指标
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.4.1" data-path="../classification_metrics.html">
|
||
|
|
|
||
|
|
<a href="../classification_metrics.html">
|
||
|
|
|
||
|
|
|
||
|
|
分类性能评估指标
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.4.2" data-path="../regression_metrics.html">
|
||
|
|
|
||
|
|
<a href="../regression_metrics.html">
|
||
|
|
|
||
|
|
|
||
|
|
回归性能评估指标
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.4.3" data-path="../cluster_metrics.html">
|
||
|
|
|
||
|
|
<a href="../cluster_metrics.html">
|
||
|
|
|
||
|
|
|
||
|
|
聚类性能评估指标
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.5" data-path="../sklearn.html">
|
||
|
|
|
||
|
|
<a href="../sklearn.html">
|
||
|
|
|
||
|
|
|
||
|
|
使用sklearn进行机器学习
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.6" >
|
||
|
|
|
||
|
|
<span>
|
||
|
|
|
||
|
|
|
||
|
|
综合实战案例
|
||
|
|
|
||
|
|
</span>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.6.1" >
|
||
|
5 years ago
|
|
||
|
|
<span>
|
||
|
|
|
||
|
|
|
||
|
|
泰坦尼克生还预测
|
||
|
|
|
||
|
|
</span>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.1.1" data-path="introduction.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="introduction.html">
|
||
|
|
|
||
|
|
|
||
|
|
简介
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.1.2" data-path="EDA.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="EDA.html">
|
||
|
|
|
||
|
|
|
||
|
|
探索性数据分析(EDA)
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.1.3" data-path="feature engerning.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="feature engerning.html">
|
||
|
|
|
||
|
|
|
||
|
|
特征工程
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter active" data-level="1.6.1.4" data-path="fit and predict.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="fit and predict.html">
|
||
|
|
|
||
|
|
|
||
|
|
构建模型进行预测
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.1.5" data-path="tuning.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="tuning.html">
|
||
|
|
|
||
|
|
|
||
|
|
调参
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.2" >
|
||
|
5 years ago
|
|
||
|
|
<span>
|
||
|
|
|
||
|
|
|
||
|
|
使用强化学习玩乒乓球游戏
|
||
|
|
|
||
|
|
</span>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<ul class="articles">
|
||
|
|
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.2.1" data-path="../pingpong/what is reinforce learning.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="../pingpong/what is reinforce learning.html">
|
||
|
|
|
||
|
|
|
||
|
|
什么是强化学习
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.2.2" data-path="../pingpong/Policy Gradient.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="../pingpong/Policy Gradient.html">
|
||
|
|
|
||
|
|
|
||
|
|
Policy Gradient原理
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
5 years ago
|
<li class="chapter " data-level="1.6.2.3" data-path="../pingpong/coding.html">
|
||
|
5 years ago
|
|
||
|
|
<a href="../pingpong/coding.html">
|
||
|
|
|
||
|
|
|
||
|
|
使用Policy Gradient玩乒乓球游戏
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
5 years ago
|
</ul>
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
<li class="chapter " data-level="1.7" data-path="../recommand.html">
|
||
|
|
|
||
|
|
<a href="../recommand.html">
|
||
|
|
|
||
|
|
|
||
|
|
实训推荐
|
||
|
|
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</li>
|
||
|
|
|
||
|
|
|
||
|
5 years ago
|
|
||
|
|
|
||
|
|
<li class="divider"></li>
|
||
|
|
|
||
|
|
<li>
|
||
|
|
<a href="https://www.gitbook.com" target="blank" class="gitbook-link">
|
||
|
|
Published with GitBook
|
||
|
|
</a>
|
||
|
|
</li>
|
||
|
|
</ul>
|
||
|
|
|
||
|
|
|
||
|
|
</nav>
|
||
|
|
|
||
|
|
|
||
|
|
</div>
|
||
|
|
|
||
|
|
<div class="book-body">
|
||
|
|
|
||
|
|
<div class="body-inner">
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<div class="book-header" role="navigation">
|
||
|
|
|
||
|
|
|
||
|
|
<!-- Title -->
|
||
|
|
<h1>
|
||
|
|
<i class="fa fa-circle-o-notch fa-spin"></i>
|
||
|
|
<a href=".." >构建模型进行预测</a>
|
||
|
|
</h1>
|
||
|
|
</div>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<div class="page-wrapper" tabindex="-1" role="main">
|
||
|
|
<div class="page-inner">
|
||
|
|
|
||
|
|
<div id="book-search-results">
|
||
|
|
<div class="search-noresults">
|
||
|
|
|
||
|
|
<section class="normal markdown-section">
|
||
|
|
|
||
|
|
<h1 id="构建模型进行预测">构建模型进行预测</h1>
|
||
|
|
<p>做好数据预处理后，可以将数据喂给我们的机器学习模型来进行训练和预测了。不过在构建模型之前，我们要使用处理训练集数据的方式来处理测试集。</p>
|
||
|
|
<pre><code class="lang-python">test_data=pd.read_csv(<span class="hljs-string">'./Titanic/test.csv'</span>)
|
||
|
|
|
||
|
|
test_data[<span class="hljs-string">'Initial'</span>]=<span class="hljs-number">0</span>
|
||
|
|
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> test_data:
|
||
|
|
test_data.loc[:, <span class="hljs-string">'Initial'</span>] = test_data.Name.str.extract(<span class="hljs-string">'([A-Za-z]+)\.'</span>,expand=<span class="hljs-keyword">False</span>) <span class="hljs-comment">#lets extract the Salutations</span>
|
||
|
|
|
||
|
|
test_data.loc[:, <span class="hljs-string">'Initial'</span>].replace([<span class="hljs-string">'Mlle'</span>,<span class="hljs-string">'Mme'</span>,<span class="hljs-string">'Ms'</span>,<span class="hljs-string">'Dr'</span>,<span class="hljs-string">'Major'</span>,<span class="hljs-string">'Lady'</span>,<span class="hljs-string">'Countess'</span>,<span class="hljs-string">'Jonkheer'</span>,<span class="hljs-string">'Col'</span>,<span class="hljs-string">'Rev'</span>,<span class="hljs-string">'Capt'</span>,<span class="hljs-string">'Sir'</span>,<span class="hljs-string">'Don'</span>],[<span class="hljs-string">'Miss'</span>,<span class="hljs-string">'Miss'</span>,<span class="hljs-string">'Miss'</span>,<span class="hljs-string">'Other'</span>,<span class="hljs-string">'Mr'</span>,<span class="hljs-string">'Mrs'</span>,<span class="hljs-string">'Mrs'</span>,<span class="hljs-string">'Other'</span>,<span class="hljs-string">'Other'</span>,<span class="hljs-string">'Other'</span>,<span class="hljs-string">'Mr'</span>,<span class="hljs-string">'Mr'</span>,<span class="hljs-string">'Mr'</span>],inplace=<span class="hljs-keyword">True</span>)
|
||
|
|
|
||
|
|
test_data.loc[(test_data.Age.isnull())&(test_data.Initial==<span class="hljs-string">'Mr'</span>),<span class="hljs-string">'Age'</span>]=<span class="hljs-number">33</span>
|
||
|
|
test_data.loc[(test_data.Age.isnull())&(test_data.Initial==<span class="hljs-string">'Mrs'</span>),<span class="hljs-string">'Age'</span>]=<span class="hljs-number">36</span>
|
||
|
|
test_data.loc[(test_data.Age.isnull())&(test_data.Initial==<span class="hljs-string">'Miss'</span>),<span class="hljs-string">'Age'</span>]=<span class="hljs-number">22</span>
|
||
|
|
test_data.loc[(test_data.Age.isnull())&(test_data.Initial==<span class="hljs-string">'Other'</span>),<span class="hljs-string">'Age'</span>]=<span class="hljs-number">46</span>
|
||
|
|
|
||
|
|
test_data[<span class="hljs-string">'Embarked'</span>].fillna(<span class="hljs-string">'S'</span>, inplace=<span class="hljs-keyword">True</span>)
|
||
|
|
|
||
|
|
test_data[<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">0</span>
|
||
|
|
test_data.loc[test_data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">16</span>,<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">0</span>
|
||
|
|
test_data.loc[(test_data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">16</span>)&(test_data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">32</span>),<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">1</span>
|
||
|
|
test_data.loc[(test_data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">32</span>)&(test_data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">48</span>),<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">2</span>
|
||
|
|
test_data.loc[(test_data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">48</span>)&(test_data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">64</span>),<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">3</span>
|
||
|
|
test_data.loc[test_data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">64</span>,<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">4</span>
|
||
|
|
|
||
|
|
test_data[<span class="hljs-string">'Family_Size'</span>]=<span class="hljs-number">0</span>
|
||
|
|
test_data[<span class="hljs-string">'Family_Size'</span>]=test_data[<span class="hljs-string">'Parch'</span>]+test_data[<span class="hljs-string">'SibSp'</span>]+<span class="hljs-number">1</span>
|
||
|
|
test_data[<span class="hljs-string">'Alone'</span>]=<span class="hljs-number">0</span>
|
||
|
|
test_data.loc[test_data.Family_Size==<span class="hljs-number">1</span>,<span class="hljs-string">'Alone'</span>]=<span class="hljs-number">1</span>
|
||
|
|
|
||
|
|
test_data[<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">0</span>
|
||
|
|
test_data.loc[test_data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">7.91</span>,<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">0</span>
|
||
|
|
test_data.loc[(test_data[<span class="hljs-string">'Fare'</span>]><span class="hljs-number">7.91</span>)&(test_data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">14.454</span>),<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">1</span>
|
||
|
|
test_data.loc[(test_data[<span class="hljs-string">'Fare'</span>]><span class="hljs-number">14.454</span>)&(test_data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">31</span>),<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">2</span>
|
||
|
|
test_data.loc[(test_data[<span class="hljs-string">'Fare'</span>]><span class="hljs-number">31</span>)&(test_data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">513</span>),<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">3</span>
|
||
|
|
|
||
|
|
test_data[<span class="hljs-string">'Sex'</span>].replace([<span class="hljs-string">'male'</span>,<span class="hljs-string">'female'</span>],[<span class="hljs-number">0</span>,<span class="hljs-number">1</span>],inplace=<span class="hljs-keyword">True</span>)
|
||
|
|
test_data[<span class="hljs-string">'Embarked'</span>].replace([<span class="hljs-string">'S'</span>,<span class="hljs-string">'C'</span>,<span class="hljs-string">'Q'</span>],[<span class="hljs-number">0</span>,<span class="hljs-number">1</span>,<span class="hljs-number">2</span>],inplace=<span class="hljs-keyword">True</span>)
|
||
|
|
test_data[<span class="hljs-string">'Initial'</span>].replace([<span class="hljs-string">'Mr'</span>,<span class="hljs-string">'Mrs'</span>,<span class="hljs-string">'Miss'</span>,<span class="hljs-string">'Master'</span>,<span class="hljs-string">'Other'</span>],[<span class="hljs-number">0</span>,<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>,<span class="hljs-number">4</span>],inplace=<span class="hljs-keyword">True</span>)
|
||
|
|
test_data[<span class="hljs-string">'Cabin'</span>].replace([<span class="hljs-string">'A'</span>, <span class="hljs-string">'B'</span>, <span class="hljs-string">'C'</span>, <span class="hljs-string">'D'</span>, <span class="hljs-string">'E'</span>, <span class="hljs-string">'F'</span>, <span class="hljs-string">'G'</span>, <span class="hljs-string">'T'</span>], [<span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>, <span class="hljs-number">5</span>, <span class="hljs-number">6</span>, <span class="hljs-number">7</span>], inplace=<span class="hljs-keyword">True</span>)
|
||
|
|
|
||
|
|
test_data.drop([<span class="hljs-string">'Name'</span>,<span class="hljs-string">'Age'</span>,<span class="hljs-string">'Ticket'</span>,<span class="hljs-string">'Fare'</span>,<span class="hljs-string">'Fare_Range'</span>,<span class="hljs-string">'PassengerId'</span>],axis=<span class="hljs-number">1</span>,inplace=<span class="hljs-keyword">True</span>)
|
||
|
|
</code></pre>
|
||
|
|
<p>然后可以使用机器学习模型来训练并预测了，这里使用的是随机森林。</p>
|
||
|
|
<pre><code class="lang-python">Y_train = data[<span class="hljs-string">'Survived'</span>]
|
||
|
|
X_train = data.drop([<span class="hljs-string">'Survived'</span>], axis=<span class="hljs-number">1</span>)
|
||
|
|
|
||
|
|
Y_test = test_data[<span class="hljs-string">'Survived'</span>]
|
||
|
|
X_test = test_data.drop([<span class="hljs-string">'Survived'</span>], axis=<span class="hljs-number">1</span>)
|
||
|
|
|
||
|
|
clf = RandomForestClassifier(n_estimators=<span class="hljs-number">10</span>)
|
||
|
|
clf.fit(X_train, Y_train)
|
||
|
|
predict = clf.predict(X_test)
|
||
|
|
print(accuracy_score(Y_test, predict))
|
||
|
|
</code></pre>
|
||
|
|
<p>此时看到预测的准确率达到了 0.8275 。</p>
|
||
|
|
|
||
|
|
|
||
|
|
</section>
|
||
|
|
|
||
|
|
</div>
|
||
|
|
<div class="search-results">
|
||
|
|
<div class="has-results">
|
||
|
|
|
||
|
|
<h1 class="search-results-title"><span class='search-results-count'></span> results matching "<span class='search-query'></span>"</h1>
|
||
|
|
<ul class="search-results-list"></ul>
|
||
|
|
|
||
|
|
</div>
|
||
|
|
<div class="no-results">
|
||
|
|
|
||
|
|
<h1 class="search-results-title">No results matching "<span class='search-query'></span>"</h1>
|
||
|
|
|
||
|
|
</div>
|
||
|
|
</div>
|
||
|
|
</div>
|
||
|
|
|
||
|
|
</div>
|
||
|
|
</div>
|
||
|
|
|
||
|
|
</div>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<a href="feature engerning.html" class="navigation navigation-prev " aria-label="Previous page: 特征工程">
|
||
|
|
<i class="fa fa-angle-left"></i>
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
<a href="tuning.html" class="navigation navigation-next " aria-label="Next page: 调参">
|
||
|
|
<i class="fa fa-angle-right"></i>
|
||
|
|
</a>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</div>
|
||
|
|
|
||
|
|
<script>
|
||
|
|
var gitbook = gitbook || [];
|
||
|
|
gitbook.push(function() {
|
||
|
5 years ago
|
gitbook.page.hasChanged({"page":{"title":"构建模型进行预测","level":"1.6.1.4","depth":3,"next":{"title":"调参","level":"1.6.1.5","depth":3,"path":"titanic/tuning.md","ref":"./titanic/tuning.md","articles":[]},"previous":{"title":"特征工程","level":"1.6.1.3","depth":3,"path":"titanic/feature engerning.md","ref":"./titanic/feature engerning.md","articles":[]},"dir":"ltr"},"config":{"gitbook":"*","theme":"default","variables":{},"plugins":["katex"],"pluginsConfig":{"katex":{},"highlight":{},"search":{},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"fontsettings":{"theme":"white","family":"sans","size":2},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":false}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"}},"file":{"path":"titanic/fit and predict.md","mtime":"2019-07-05T01:21:38.971Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2019-07-06T07:31:21.537Z"},"basePath":"..","book":{"language":""}});
|
||
|
5 years ago
|
});
|
||
|
|
</script>
|
||
|
|
</div>
|
||
|
|
|
||
|
|
|
||
|
|
<script src="../gitbook/gitbook.js"></script>
|
||
|
|
<script src="../gitbook/theme.js"></script>
|
||
|
|
|
||
|
|
|
||
|
|
<script src="../gitbook/gitbook-plugin-search/search-engine.js"></script>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<script src="../gitbook/gitbook-plugin-search/search.js"></script>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<script src="../gitbook/gitbook-plugin-lunr/lunr.min.js"></script>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<script src="../gitbook/gitbook-plugin-lunr/search-lunr.js"></script>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<script src="../gitbook/gitbook-plugin-sharing/buttons.js"></script>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
<script src="../gitbook/gitbook-plugin-fontsettings/fontsettings.js"></script>
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
</body>
|
||
|
|
</html>
|
||
|
|
|