决策树算法(二)

朴灿烈づ我的快乐病毒、 2023-07-09 02:03 61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]

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

[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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# 4.5 决策树算法api #

class sklearn.tree.DecisionTreeClassifier(criterion=’gini’, max\_depth=None,random\_state=None)

*  **criterion**
    
     *  特征选择标准
     *  "gini"或者"entropy"，前者代表基尼系数，后者代表信息增益。一默认"gini"，即CART算法。
 *  **min\_samples\_split**
    
     *  内部节点再划分所需最小样本数
     *  这个值限制了子树继续划分的条件，如果某节点的样本数少于min\_samples\_split，则不会继续再尝试选择最优特征来进行划分。 默认是2.如果样本量不大，不需要管这个值。如果样本量数量级非常大，则推荐增大这个值。我之前的一个项目例子，有大概10万样本，建立决策树时，我选择了min\_samples\_split=10。可以作为参考。
 *  **min\_samples\_leaf**
    
     *  叶子节点最少样本数
     *  这个值限制了叶子节点最少的样本数，如果某叶子节点数目小于样本数，则会和兄弟节点一起被剪枝。 默认是1,可以输入最少的样本数的整数，或者最少样本数占样本总数的百分比。如果样本量不大，不需要管这个值。如果样本量数量级非常大，则推荐增大这个值。之前的10万样本项目使用min\_samples\_leaf的值为5，仅供参考。
 *  **max\_depth**
    
     *  决策树最大深度
     *  决策树的最大深度，默认可以不输入，如果不输入的话，决策树在建立子树的时候不会限制子树的深度。一般来说，数据少或者特征少的时候可以不管这个值。如果模型样本量多，特征也多的情况下，推荐限制这个最大深度，具体的取值取决于数据的分布。常用的可以取值10-100之间
 *  random\_state
    
     *  随机数种子

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

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'},
     {'pclass': '2nd', 'age': 36.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 19.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 28.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 17.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 21.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 25.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 19.0, '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': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 26.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 21.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 48.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 27.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 46.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 26.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 29.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 35.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 38.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 32.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 16.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 30.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 18.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 16.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 33.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 17.0, 'sex': 'male'},
     {'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': 'male'},
     {'pclass': '1st', 'age': 36.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 30.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 20.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 33.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 52.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 35.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 25.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 45.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 50.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 52.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 20.0, 'sex': 'female'},
     {'pclass': '3rd', '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': '2nd', 'age': 32.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 34.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 33.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 21.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 45.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 43.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 59.0, 'sex': 'male'},
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     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 35.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 24.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 45.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 24.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 50.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 56.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 32.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 19.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 18.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 20.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 45.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 22.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 20.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 50.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 30.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 24.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 40.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 21.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 52.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 45.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 11.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 23.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 26.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 40.0, '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': 'female'},
     {'pclass': '1st', 'age': 49.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 18.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 18.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 9.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 25.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 35.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 32.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 21.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 32.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 45.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 30.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 26.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 30.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 21.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 25.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 20.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 36.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 27.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 24.0, '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': 18.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 56.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 16.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 18.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', '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': 'male'},
     {'pclass': '1st', 'age': 64.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 46.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 18.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 46.0, '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': 'female'},
     {'pclass': '3rd', 'age': 29.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 18.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 33.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 34.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 0.8333, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 58.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 60.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 23.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 44.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 71.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 13.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 58.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 4.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 16.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 33.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 33.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 48.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 28.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 55.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 54.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 71.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 47.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 21.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 21.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 24.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 23.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 18.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 54.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 17.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 6.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 45.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 36.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 55.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 26.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 65.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 27.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 22.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '2nd', 'age': 7.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 30.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '1st', 'age': 39.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 19.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 19.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 20.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 56.0, 'sex': 'male'},
     {'pclass': '2nd', 'age': 38.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 23.0, 'sex': 'female'},
     {'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(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=False, 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=\['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', '女性', '男性'\])

#  #

[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