决策树算法(三)

绝地灬酷狼 2023-07-09 02:03 1阅读 0赞

# ![20191009191333910.png][][日萌社][Link 1] #

[人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新）][AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]

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[决策树算法(一)][Link 2]

[决策树算法(二)][Link 3]

[决策树算法(三)][Link 4]

[决策树算法(四)][Link 5]

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# 4.6 案例：泰坦尼克号乘客生存预测 #

## 1 案例背景 ##

泰坦尼克号沉没是历史上最臭名昭着的沉船之一。1912年4月15日，在她的处女航中，泰坦尼克号在与冰山相撞后沉没，在2224名乘客和机组人员中造成1502人死亡。这场耸人听闻的悲剧震惊了国际社会，并为船舶制定了更好的安全规定。 造成海难失事的原因之一是乘客和机组人员没有足够的救生艇。尽管幸存下沉有一些运气因素，但有些人比其他人更容易生存，例如妇女，儿童和上流社会。 在这个案例中，我们要求您完成对哪些人可能存活的分析。特别是，我们要求您运用机器学习工具来预测哪些乘客幸免于悲剧。

> 案例：[https://www.kaggle.com/c/titanic/overview][https_www.kaggle.com_c_titanic_overview]

我们提取到的数据集中的特征包括票的类别，是否存活，乘坐班次，年龄，登陆home.dest，房间，船和性别等。

> 数据：[http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt][http_biostat.mc.vanderbilt.edu_wiki_pub_Main_DataSets_titanic.txt]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70][]

经过观察数据得到:

*  **1 乘坐班是指乘客班（1，2，3），是社会经济阶层的代表。**
 *  **2 其中age数据存在缺失。**

## 2 步骤分析 ##

*  1.获取数据
 *  2.数据基本处理
    
     *  2.1 确定特征值,目标值
     *  2.2 缺失值处理
     *  2.3 数据集划分
 *  3.特征工程(字典特征抽取)
 *  4.机器学习(决策树)
 *  5.模型评估

## 3 代码实现 ##

*  导入需要的模块

import pandas as pd
    import numpy as np
    from sklearn.feature_extraction import DictVectorizer
    from sklearn.model_selection import train_test_split
    from sklearn.tree import DecisionTreeClassifier, export_graphviz

*  1.获取数据

# 1、获取数据
    titan = pd.read_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt")

*  2.数据基本处理
    
     *  2.1 确定特征值,目标值
    
        x = titan[["pclass", "age", "sex"]]
        y = titan["survived"]
    
     *  2.2 缺失值处理
    
        # 缺失值需要处理，将特征当中有类别的这些特征进行字典特征抽取
        x['age'].fillna(x['age'].mean(), inplace=True)
    
     *  2.3 数据集划分
    
        x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=22)
 *  3.特征工程(字典特征抽取)

特征中出现类别符号，需要进行one-hot编码处理(DictVectorizer)

x.to\_dict(orient="records") 需要将数组特征转换成字典数据

# 对于x转换成字典数据x.to_dict(orient="records")
    # [{"pclass": "1st", "age": 29.00, "sex": "female"}, {}]
    
    transfer = DictVectorizer(sparse=False)
    
    x_train = transfer.fit_transform(x_train.to_dict(orient="records"))
    x_test = transfer.fit_transform(x_test.to_dict(orient="records"))

*  4.决策树模型训练和模型评估

决策树API当中，如果没有指定max\_depth那么会根据信息熵的条件直到最终结束。这里我们可以指定树的深度来进行限制树的大小

# 4.机器学习(决策树)
    estimator = DecisionTreeClassifier(criterion="entropy", max_depth=5)
    estimator.fit(x_train, y_train)
    
    # 5.模型评估
    estimator.score(x_test, y_test)
    
    estimator.predict(x_test)

决策树的结构是可以直接显示

## 4 决策树可视化 ##

### 4.1 保存树的结构到dot文件 ###

*  sklearn.tree.export\_graphviz() 该函数能够导出DOT格式
    
     *  tree.export\_graphviz(estimator,out\_file='tree.dot’,feature\_names=\[‘’,’’\])

export_graphviz(estimator, out_file="./data/tree.dot", feature_names=['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', '女性', '男性'])

dot文件当中的内容如下

digraph Tree {
    node [shape=box] ;
    0 [label="petal length (cm) <= 2.45\nentropy = 1.584\nsamples = 112\nvalue = [39, 37, 36]"] ;
    1 [label="entropy = 0.0\nsamples = 39\nvalue = [39, 0, 0]"] ;
    0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
    2 [label="petal width (cm) <= 1.75\nentropy = 1.0\nsamples = 73\nvalue = [0, 37, 36]"] ;
    0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
    3 [label="petal length (cm) <= 5.05\nentropy = 0.391\nsamples = 39\nvalue = [0, 36, 3]"] ;
    2 -> 3 ;
    4 [label="sepal length (cm) <= 4.95\nentropy = 0.183\nsamples = 36\nvalue = [0, 35, 1]"] ;
    3 -> 4 ;
    5 [label="petal length (cm) <= 3.9\nentropy = 1.0\nsamples = 2\nvalue = [0, 1, 1]"] ;
    4 -> 5 ;
    6 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1, 0]"] ;
    5 -> 6 ;
    7 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 0, 1]"] ;
    5 -> 7 ;
    8 [label="entropy = 0.0\nsamples = 34\nvalue = [0, 34, 0]"] ;
    4 -> 8 ;
    9 [label="petal width (cm) <= 1.55\nentropy = 0.918\nsamples = 3\nvalue = [0, 1, 2]"] ;
    3 -> 9 ;
    10 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 0, 2]"] ;
    9 -> 10 ;
    11 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1, 0]"] ;
    9 -> 11 ;
    12 [label="petal length (cm) <= 4.85\nentropy = 0.191\nsamples = 34\nvalue = [0, 1, 33]"] ;
    2 -> 12 ;
    13 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1, 0]"] ;
    12 -> 13 ;
    14 [label="entropy = 0.0\nsamples = 33\nvalue = [0, 0, 33]"] ;
    12 -> 14 ;
    }

那么这个结构不能看清结构，所以可以在一个网站上显示

### 4.2 网站显示结构 ###

*  [http://webgraphviz.com/][http_webgraphviz.com]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 1][]

![20200227082017400.png][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 2][]

## 5 决策树总结 ##

*  优点：
    
     *  简单的理解和解释，树木可视化。
 *  缺点：
    
     *  **决策树学习者可以创建不能很好地推广数据的过于复杂的树,容易发生过拟合。**
 *  改进：
    
     *  减枝cart算法
     *  **随机森林**（集成学习的一种）

**注：企业重要决策，由于决策树很好的分析能力，在决策过程应用较多， 可以选择特征**

--------------------

## 6 小结 ##

*  案例流程分析【了解】
    
     *  1.获取数据
     *  2.数据基本处理
        
         *  2.1 确定特征值,目标值
         *  2.2 缺失值处理
         *  2.3 数据集划分
     *  3.特征工程(字典特征抽取)
     *  4.机器学习(决策树)
     *  5.模型评估
 *  决策树可视化【了解】
    
     *  sklearn.tree.export\_graphviz()
 *  决策树优缺点总结【知道】
    
     *  优点：
        
         *  简单的理解和解释，树木可视化。
     *  缺点：
        
         *  决策树学习者可以创建不能很好地推广数据的过于复杂的树,容易发生过拟合。
     *  改进：
        
         *  减枝cart算法
         *  随机森林（集成学习的一种）

--------------------

In \[1\]:

\# 1.获取数据  
\# 2.数据基本处理  
\# 2.1 确定特征值,目标值  
\# 2.2 缺失值处理  
\# 2.3 数据集划分  
\# 3.特征工程(字典特征抽取)  
\# 4.机器学习(决策树)  
\# 5.模型评估

In \[2\]:

import pandas as pd  
import numpy as np  
from sklearn.model\_selection import train\_test\_split  
from sklearn.feature\_extraction import DictVectorizer  
from sklearn.tree import DecisionTreeClassifier, export\_graphviz

In \[3\]:

\# 1.获取数据  
titan = pd.read\_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt")

In \[4\]:

titan

Out\[4\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>row.names</th> 
   <th>pclass</th> 
   <th>survived</th> 
   <th>name</th> 
   <th>age</th> 
   <th>embarked</th> 
   <th>home.dest</th> 
   <th>room</th> 
   <th>ticket</th> 
   <th>boat</th> 
   <th>sex</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>1</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Allen, Miss Elisabeth Walton</td> 
   <td>29.0000</td> 
   <td>Southampton</td> 
   <td>St Louis, MO</td> 
   <td>B-5</td> 
   <td>24160 L221</td> 
   <td>2</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>2</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Allison, Miss Helen Loraine</td> 
   <td>2.0000</td> 
   <td>Southampton</td> 
   <td>Montreal, PQ / Chesterville, ON</td> 
   <td>C26</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>3</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Allison, Mr Hudson Joshua Creighton</td> 
   <td>30.0000</td> 
   <td>Southampton</td> 
   <td>Montreal, PQ / Chesterville, ON</td> 
   <td>C26</td> 
   <td>NaN</td> 
   <td>(135)</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>4</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Allison, Mrs Hudson J.C. (Bessie Waldo Daniels)</td> 
   <td>25.0000</td> 
   <td>Southampton</td> 
   <td>Montreal, PQ / Chesterville, ON</td> 
   <td>C26</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>5</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Allison, Master Hudson Trevor</td> 
   <td>0.9167</td> 
   <td>Southampton</td> 
   <td>Montreal, PQ / Chesterville, ON</td> 
   <td>C22</td> 
   <td>NaN</td> 
   <td>11</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>5</th> 
   <td>6</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Anderson, Mr Harry</td> 
   <td>47.0000</td> 
   <td>Southampton</td> 
   <td>New York, NY</td> 
   <td>E-12</td> 
   <td>NaN</td> 
   <td>3</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>6</th> 
   <td>7</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Andrews, Miss Kornelia Theodosia</td> 
   <td>63.0000</td> 
   <td>Southampton</td> 
   <td>Hudson, NY</td> 
   <td>D-7</td> 
   <td>13502 L77</td> 
   <td>10</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>7</th> 
   <td>8</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Andrews, Mr Thomas, jr</td> 
   <td>39.0000</td> 
   <td>Southampton</td> 
   <td>Belfast, NI</td> 
   <td>A-36</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>8</th> 
   <td>9</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Appleton, Mrs Edward Dale (Charlotte Lamson)</td> 
   <td>58.0000</td> 
   <td>Southampton</td> 
   <td>Bayside, Queens, NY</td> 
   <td>C-101</td> 
   <td>NaN</td> 
   <td>2</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>9</th> 
   <td>10</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Artagaveytia, Mr Ramon</td> 
   <td>71.0000</td> 
   <td>Cherbourg</td> 
   <td>Montevideo, Uruguay</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>(22)</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>10</th> 
   <td>11</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Astor, Colonel John Jacob</td> 
   <td>47.0000</td> 
   <td>Cherbourg</td> 
   <td>New York, NY</td> 
   <td>NaN</td> 
   <td>17754 L224 10s 6d</td> 
   <td>(124)</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>11</th> 
   <td>12</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Astor, Mrs John Jacob (Madeleine Talmadge Force)</td> 
   <td>19.0000</td> 
   <td>Cherbourg</td> 
   <td>New York, NY</td> 
   <td>NaN</td> 
   <td>17754 L224 10s 6d</td> 
   <td>4</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>12</th> 
   <td>13</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Aubert, Mrs Leontine Pauline</td> 
   <td>NaN</td> 
   <td>Cherbourg</td> 
   <td>Paris, France</td> 
   <td>B-35</td> 
   <td>17477 L69 6s</td> 
   <td>9</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>13</th> 
   <td>14</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Barkworth, Mr Algernon H.</td> 
   <td>NaN</td> 
   <td>Southampton</td> 
   <td>Hessle, Yorks</td> 
   <td>A-23</td> 
   <td>NaN</td> 
   <td>B</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>14</th> 
   <td>15</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Baumann, Mr John D.</td> 
   <td>NaN</td> 
   <td>Southampton</td> 
   <td>New York, NY</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>15</th> 
   <td>16</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Baxter, Mrs James (Helene DeLaudeniere Chaput)</td> 
   <td>50.0000</td> 
   <td>Cherbourg</td> 
   <td>Montreal, PQ</td> 
   <td>B-58/60</td> 
   <td>NaN</td> 
   <td>6</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>16</th> 
   <td>17</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Baxter, Mr Quigg Edmond</td> 
   <td>24.0000</td> 
   <td>Cherbourg</td> 
   <td>Montreal, PQ</td> 
   <td>B-58/60</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>17</th> 
   <td>18</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Beattie, Mr Thomson</td> 
   <td>36.0000</td> 
   <td>Cherbourg</td> 
   <td>Winnipeg, MN</td> 
   <td>C-6</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>18</th> 
   <td>19</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Beckwith, Mr Richard Leonard</td> 
   <td>37.0000</td> 
   <td>Southampton</td> 
   <td>New York, NY</td> 
   <td>D-35</td> 
   <td>NaN</td> 
   <td>5</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>19</th> 
   <td>20</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Beckwith, Mrs Richard Leonard (Sallie Monypeny)</td> 
   <td>47.0000</td> 
   <td>Southampton</td> 
   <td>New York, NY</td> 
   <td>D-35</td> 
   <td>NaN</td> 
   <td>5</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>20</th> 
   <td>21</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Behr, Mr Karl Howell</td> 
   <td>26.0000</td> 
   <td>Cherbourg</td> 
   <td>New York, NY</td> 
   <td>C-148</td> 
   <td>NaN</td> 
   <td>5</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>21</th> 
   <td>22</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Birnbaum, Mr Jakob</td> 
   <td>25.0000</td> 
   <td>Cherbourg</td> 
   <td>San Francisco, CA</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>(148)</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>22</th> 
   <td>23</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Bishop, Mr Dickinson H.</td> 
   <td>25.0000</td> 
   <td>Cherbourg</td> 
   <td>Dowagiac, MI</td> 
   <td>B-49</td> 
   <td>NaN</td> 
   <td>7</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>23</th> 
   <td>24</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Bishop, Mrs Dickinson H. (Helen Walton)</td> 
   <td>19.0000</td> 
   <td>Cherbourg</td> 
   <td>Dowagiac, MI</td> 
   <td>B-49</td> 
   <td>NaN</td> 
   <td>7</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>24</th> 
   <td>25</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Bjornstrm-Steffansson, Mr Mauritz Hakan</td> 
   <td>28.0000</td> 
   <td>Southampton</td> 
   <td>Stockholm, Sweden / Washington, DC</td> 
   <td>NaN</td> 
   <td></td> 
   <td>D</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>25</th> 
   <td>26</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Blackwell, Mr Stephen Weart</td> 
   <td>45.0000</td> 
   <td>Southampton</td> 
   <td>Trenton, NJ</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>(241)</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>26</th> 
   <td>27</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Blank, Mr Henry</td> 
   <td>39.0000</td> 
   <td>Cherbourg</td> 
   <td>Glen Ridge, NJ</td> 
   <td>A-31</td> 
   <td>NaN</td> 
   <td>7</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>27</th> 
   <td>28</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Bonnell, Miss Caroline</td> 
   <td>30.0000</td> 
   <td>Southampton</td> 
   <td>Youngstown, OH</td> 
   <td>C-7</td> 
   <td>NaN</td> 
   <td>8</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>28</th> 
   <td>29</td> 
   <td>1st</td> 
   <td>1</td> 
   <td>Bonnell, Miss Elizabeth</td> 
   <td>58.0000</td> 
   <td>Southampton</td> 
   <td>Birkdale, England Cleveland, Ohio</td> 
   <td>C-103</td> 
   <td>NaN</td> 
   <td>8</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>29</th> 
   <td>30</td> 
   <td>1st</td> 
   <td>0</td> 
   <td>Borebank, Mr John James</td> 
   <td>NaN</td> 
   <td>Southampton</td> 
   <td>London / Winnipeg, MB</td> 
   <td>D-21/2</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>1283</th> 
   <td>1284</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Vestrom, Miss Hulda Amanda Adolfina</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1284</th> 
   <td>1285</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Vonk, Mr Jenko</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1285</th> 
   <td>1286</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Ware, Mr Frederick</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1286</th> 
   <td>1287</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Warren, Mr Charles William</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1287</th> 
   <td>1288</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Wazli, Mr Yousif</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1288</th> 
   <td>1289</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Webber, Mr James</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1289</th> 
   <td>1290</td> 
   <td>3rd</td> 
   <td>1</td> 
   <td>Wennerstrom, Mr August Edvard</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1290</th> 
   <td>1291</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Wenzel, Mr Linhart</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1291</th> 
   <td>1292</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Widegren, Mr Charles Peter</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1292</th> 
   <td>1293</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Wiklund, Mr Jacob Alfred</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1293</th> 
   <td>1294</td> 
   <td>3rd</td> 
   <td>1</td> 
   <td>Wilkes, Mrs Ellen</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1294</th> 
   <td>1295</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Willer, Mr Aaron</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1295</th> 
   <td>1296</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Willey, Mr Edward</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1296</th> 
   <td>1297</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Williams, Mr Howard Hugh</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1297</th> 
   <td>1298</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Williams, Mr Leslie</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1298</th> 
   <td>1299</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Windelov, Mr Einar</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1299</th> 
   <td>1300</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Wirz, Mr Albert</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1300</th> 
   <td>1301</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Wiseman, Mr Phillippe</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1301</th> 
   <td>1302</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Wittevrongel, Mr Camiel</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1302</th> 
   <td>1303</td> 
   <td>3rd</td> 
   <td>1</td> 
   <td>Yalsevac, Mr Ivan</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1303</th> 
   <td>1304</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Yasbeck, Mr Antoni</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1304</th> 
   <td>1305</td> 
   <td>3rd</td> 
   <td>1</td> 
   <td>Yasbeck, Mrs Antoni</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1305</th> 
   <td>1306</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Youssef, Mr Gerios</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1306</th> 
   <td>1307</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Zabour, Miss Hileni</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1307</th> 
   <td>1308</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Zabour, Miss Tamini</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1308</th> 
   <td>1309</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Zakarian, Mr Artun</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1309</th> 
   <td>1310</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Zakarian, Mr Maprieder</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1310</th> 
   <td>1311</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Zenn, Mr Philip</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>1311</th> 
   <td>1312</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Zievens, Rene</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1312</th> 
   <td>1313</td> 
   <td>3rd</td> 
   <td>0</td> 
   <td>Zimmerman, Leo</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>NaN</td> 
   <td>male</td> 
  </tr> 
 </tbody> 
</table>

1313 rows × 11 columns

In \[5\]:

titan.describe()

Out\[5\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>row.names</th> 
   <th>survived</th> 
   <th>age</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>count</th> 
   <td>1313.000000</td> 
   <td>1313.000000</td> 
   <td>633.000000</td> 
  </tr> 
  <tr> 
   <th>mean</th> 
   <td>657.000000</td> 
   <td>0.341965</td> 
   <td>31.194181</td> 
  </tr> 
  <tr> 
   <th>std</th> 
   <td>379.174762</td> 
   <td>0.474549</td> 
   <td>14.747525</td> 
  </tr> 
  <tr> 
   <th>min</th> 
   <td>1.000000</td> 
   <td>0.000000</td> 
   <td>0.166700</td> 
  </tr> 
  <tr> 
   <th>25%</th> 
   <td>329.000000</td> 
   <td>0.000000</td> 
   <td>21.000000</td> 
  </tr> 
  <tr> 
   <th>50%</th> 
   <td>657.000000</td> 
   <td>0.000000</td> 
   <td>30.000000</td> 
  </tr> 
  <tr> 
   <th>75%</th> 
   <td>985.000000</td> 
   <td>1.000000</td> 
   <td>41.000000</td> 
  </tr> 
  <tr> 
   <th>max</th> 
   <td>1313.000000</td> 
   <td>1.000000</td> 
   <td>71.000000</td> 
  </tr> 
 </tbody> 
</table>

In \[6\]:

\# 2.数据基本处理  
\# 2.1 确定特征值,目标值  
x = titan\[\["pclass", "age", "sex"\]\]  
y = titan\["survived"\]

In \[7\]:

x.head()

Out\[7\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>pclass</th> 
   <th>age</th> 
   <th>sex</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>1st</td> 
   <td>29.0000</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>1st</td> 
   <td>2.0000</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>1st</td> 
   <td>30.0000</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>1st</td> 
   <td>25.0000</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>1st</td> 
   <td>0.9167</td> 
   <td>male</td> 
  </tr> 
 </tbody> 
</table>

In \[8\]:

y.head()

Out\[8\]:

0    1
    1    0
    2    0
    3    0
    4    1
    Name: survived, dtype: int64

In \[9\]:

\# 2.2 缺失值处理  
x\['age'\].fillna(value=titan\["age"\].mean(), inplace=True)

In \[10\]:

x.head()

Out\[10\]:

In \[11\]:

\# 2.3 数据集划分  
x\_train, x\_test, y\_train, y\_test = train\_test\_split(x, y, random\_state=22, test\_size=0.2)

In \[12\]:

\# 3.特征工程(字典特征抽取)

In \[13\]:

x\_train.head()

Out\[13\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>pclass</th> 
   <th>age</th> 
   <th>sex</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>649</th> 
   <td>3rd</td> 
   <td>45.000000</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>1078</th> 
   <td>3rd</td> 
   <td>31.194181</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>59</th> 
   <td>1st</td> 
   <td>31.194181</td> 
   <td>female</td> 
  </tr> 
  <tr> 
   <th>201</th> 
   <td>1st</td> 
   <td>18.000000</td> 
   <td>male</td> 
  </tr> 
  <tr> 
   <th>61</th> 
   <td>1st</td> 
   <td>31.194181</td> 
   <td>female</td> 
  </tr> 
 </tbody> 
</table>

In \[14\]:

x\_train = x\_train.to\_dict(orient="records")  
x\_test = x\_test.to\_dict(orient="records")

In \[15\]:

x\_train

Out\[15\]:

[{'pclass': '3rd', 'age': 45.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 18.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 6.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 27.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 21.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 4.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 13.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 30.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 30.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 50.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 22.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 26.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 49.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 62.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 32.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 64.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 23.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 55.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 24.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 6.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 10.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 53.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 18.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
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     {'pclass': '3rd', 'age': 19.0, 'sex': 'male'},
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     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 42.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 23.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 25.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 16.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 42.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 2.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 36.0, 'sex': 'male'},
     ...]

In \[16\]:

transfer = DictVectorizer()

x\_train = transfer.fit\_transform(x\_train)  
x\_test = transfer.fit\_transform(x\_test)

In \[17\]:

x\_test

Out\[17\]:

<263x6 sparse matrix of type '<class 'numpy.float64'>'
    	with 789 stored elements in Compressed Sparse Row format>

In \[18\]:

\# 4.机器学习(决策树)  
estimator = DecisionTreeClassifier(max\_depth=15)  
estimator.fit(x\_train, y\_train)

Out\[18\]:

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                           max_depth=15, max_features=None, max_leaf_nodes=None,
                           min_impurity_decrease=0.0, min_impurity_split=None,
                           min_samples_leaf=1, min_samples_split=2,
                           min_weight_fraction_leaf=0.0, presort='deprecated',
                           random_state=None, splitter='best')

In \[19\]:

\# 5.模型评估  
y\_pre = estimator.predict(x\_test)

In \[20\]:

y\_pre

Out\[20\]:

array([0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
           0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,
           0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
           0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
           1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,
           0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
           1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,
           1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1])

In \[21\]:

ret = estimator.score(x\_test, y\_test)  
print(ret)

0.779467680608365

In \[22\]:

export\_graphviz(estimator, out\_file="./data/tree.dot", feature\_names=\['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', '女性', '男性'\])

In \[23\]:

\# 实例化一个随机森林  
from sklearn.ensemble import RandomForestClassifier  
rf = RandomForestClassifier()

In \[26\]:

\# 通过超参数调优  
from sklearn.model\_selection import GridSearchCV

param = \{"n\_estimators":\[100, 120, 300\], "max\_depth":\[3, 7, 11\]\}

gc = GridSearchCV(rf, param\_grid=param, cv=3)

gc.fit(x\_train, y\_train)

Out\[26\]:

GridSearchCV(cv=3, error_score=nan,
                 estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                                  class_weight=None,
                                                  criterion='gini', max_depth=None,
                                                  max_features='auto',
                                                  max_leaf_nodes=None,
                                                  max_samples=None,
                                                  min_impurity_decrease=0.0,
                                                  min_impurity_split=None,
                                                  min_samples_leaf=1,
                                                  min_samples_split=2,
                                                  min_weight_fraction_leaf=0.0,
                                                  n_estimators=100, n_jobs=None,
                                                  oob_score=False,
                                                  random_state=None, verbose=0,
                                                  warm_start=False),
                 iid='deprecated', n_jobs=None,
                 param_grid={'max_depth': [3, 7, 11],
                             'n_estimators': [100, 120, 300]},
                 pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
                 scoring=None, verbose=0)

In \[27\]:

print("随机森林预测结果是:\\n", gc.score(x\_test, y\_test))

随机森林预测结果是:
     0.779467680608365

[20191009191333910.png]: https://img-blog.csdnimg.cn/20191009191333910.png
[Link 1]: http://www.rimengshe.com/
[AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]: https://blog.csdn.net/zimiao552147572/article/details/88867161
[Link 2]: https://blog.csdn.net/zimiao552147572/article/details/104529398
[Link 3]: https://blog.csdn.net/zimiao552147572/article/details/104529521
[Link 4]: https://blog.csdn.net/zimiao552147572/article/details/104529535
[Link 5]: https://blog.csdn.net/zimiao552147572/article/details/104529586
[https_www.kaggle.com_c_titanic_overview]: https://www.kaggle.com/c/titanic/overview
[http_biostat.mc.vanderbilt.edu_wiki_pub_Main_DataSets_titanic.txt]: http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200227081944691.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70
[http_webgraphviz.com]: http://webgraphviz.com/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200227082010758.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70
[20200227082017400.png]: https://img-blog.csdnimg.cn/20200227082017400.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20200227082037693.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70