决策树算法(四)

r囧r小猫 2023-07-09 02:03 107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]

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

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(随机森林)  
\# 5.模型评估

In \[20\]:

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

In \[4\]:

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

In \[5\]:

titan

Out\[5\]:

<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 \[6\]:

titan.describe()

Out\[6\]:

<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 \[7\]:

\# 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 \[8\]:

x.head()  
Out\[8\]:

<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 \[9\]:

y.head()

Out\[9\]:

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

In \[10\]:

\# 2.2 缺失值处理

x['age'].fillna(value=titan["age"].mean(), inplace=True)

In \[11\]:

x.head()

Out\[11\]:

In \[12\]:

\# 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 \[13\]:

\# 3.特征工程(字典特征抽取)

In \[14\]:

x\_train.head()

Out\[14\]:

<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 \[15\]:

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

In \[16\]:

x\_train

Out\[16\]:

[{'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'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '1st', 'age': 47.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 38.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 51.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 36.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 23.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '3rd', 'age': 6.0, 'sex': 'female'},
     {'pclass': '1st', 'age': 58.0, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 4.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 20.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 35.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
     {'pclass': '2nd', 'age': 12.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 19.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 64.0, 'sex': 'male'},
     {'pclass': '1st', 'age': 27.0, 'sex': 'female'},
     {'pclass': '2nd', 'age': 34.0, 'sex': 'male'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
     {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
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     {'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 \[17\]:

transfer = DictVectorizer()

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

In \[18\]:

x\_test

Out\[18\]:

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

In \[21\]:

\# 4.机器学习(随机森林+cv)  
estimator = RandomForestClassifier()

param\_grid = \{"n\_estimators": \[120,200,300,500,800,1200\], "max\_depth": \[5, 8, 15, 25, 30\]\}  
estimator = GridSearchCV(estimator, param\_grid=param\_grid, cv=3)

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

Out[21]:

GridSearchCV(cv=3, error_score='raise',
           estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
                max_depth=None, max_features='auto', 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, n_estimators=10, n_jobs=1,
                oob_score=False, random_state=None, verbose=0,
                warm_start=False),
           fit_params=None, iid=True, n_jobs=1,
           param_grid={'n_estimators': [120, 200, 300, 500, 800, 1200], 'max_depth': [5, 8, 15, 25, 30]},
           pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
           scoring=None, verbose=0)

In \[22\]:

estimator.score(x\_test, y\_test)

Out\[22\]:

0.7908745247148289

In \[23\]:

estimator.best\_estimator\_

Out\[23\]:

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
                max_depth=5, max_features='auto', 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, n_estimators=300, n_jobs=1,
                oob_score=False, random_state=None, verbose=0,
                warm_start=False)

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

# 4.7 回归决策树 #

前面已经讲到，关于数据类型，我们主要可以把其分为两类，**连续型数据和离散型数据。**在面对不同数据时，决策树也可以分为两大类型：

*  **分类决策树和回归决策树。**
 *  **前者主要用于处理离散型数据，后者主要用于处理连续型数据。**

## 1.原理概述 ##

不管是回归决策树还是分类决策树，都会存在两个核心问题：

*  如何选择划分点？
 *  如何决定叶节点的输出值？

一个回归树对应着输入空间（即特征空间）的一个划分以及在划分单元上的输出值。分类树中，我们采用信息论中的方法，通过计算选择最佳划分点。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 1][]

## 2.算法描述 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 2][]

## 3.简单实例 ##

为了易于理解，接下来通过一个简单实例加深对回归决策树的理解。

训练数据见下表，目标是得到一棵最小二乘回归树。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 4][]

同理，得到其他各切分点的子区域输出值，如下表：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 5][]

同理，计算得到其他各切分点的损失函数值，可获得下表：

显然取 s=6.5时，m(s)最小。因此，第一个划分变量【j=x,s=6.5】

![20200227082352609.png][]

**（3）调用步骤 (1)、(2)，继续划分：**

对R1继续进行划分:

取切分点\[1.5,2.5,3.5,4.5,5.5\]，则各区域的输出值c如下表：

计算损失函数值m(s)：

s=3.5时，m(s)最小。

**（4）生成回归树**

**假设**在生成**3个区域**之后停止划分，那么最终生成的回归树形式如下：

![20200227082405605.png][]

### 3.2 回归决策树和线性回归对比 ###

import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.tree import DecisionTreeRegressor
    from sklearn import linear_model
    
    # 生成数据
    x = np.array(list(range(1, 11))).reshape(-1, 1)
    y = np.array([5.56, 5.70, 5.91, 6.40, 6.80, 7.05, 8.90, 8.70, 9.00, 9.05])
    
    # 训练模型
    model1 = DecisionTreeRegressor(max_depth=1)
    model2 = DecisionTreeRegressor(max_depth=3)
    model3 = linear_model.LinearRegression()
    model1.fit(x, y)
    model2.fit(x, y)
    model3.fit(x, y)
    
    # 模型预测
    X_test = np.arange(0.0, 10.0, 0.01).reshape(-1, 1)  # 生成1000个数,用于预测模型
    X_test.shape
    y_1 = model1.predict(X_test)
    y_2 = model2.predict(X_test)
    y_3 = model3.predict(X_test)
    
    # 结果可视化
    plt.figure(figsize=(10, 6), dpi=100)
    plt.scatter(x, y, label="data")
    plt.plot(X_test, y_1,label="max_depth=1")
    plt.plot(X_test, y_2, label="max_depth=3")
    plt.plot(X_test, y_3, label='liner regression')
    
    plt.xlabel("data")
    plt.ylabel("target")
    plt.title("Decision Tree Regression")
    plt.legend()
    
    plt.show()

> 结果展示

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 6][]

## 4 小结 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 7][]

In \[1\]:

import numpy as np  
import matplotlib.pyplot as plt  
from sklearn.tree import DecisionTreeRegressor  
from sklearn.linear\_model import LinearRegression

In \[9\]:

\# 生成数据  
x = np.array(list(range(1, 11))).reshape(-1,1)  
y = (\[5.56, 5.70, 5.91, 6.40, 6.80, 7.05, 8.90, 8.70, 9.00, 9.05\])

In \[8\]:

Out\[8\]:

array([[ 1],
           [ 2],
           [ 3],
           [ 4],
           [ 5],
           [ 6],
           [ 7],
           [ 8],
           [ 9],
           [10]])

In \[10\]:

Out\[10\]:

[5.56, 5.7, 5.91, 6.4, 6.8, 7.05, 8.9, 8.7, 9.0, 9.05]

In \[11\]:

\# 模型训练  
m1 = DecisionTreeRegressor(max\_depth=1)  
m2 = DecisionTreeRegressor(max\_depth=3)  
m3 = LinearRegression()

m1.fit(x, y)  
m2.fit(x, y)  
m3.fit(x, y)

Out\[11\]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

In \[14\]:

\# 模型预测  
x\_test = np.arange(0, 10, 0.01).reshape(-1, 1)

In \[15\]:

y\_1 = m1.predict(x\_test)  
y\_2 = m2.predict(x\_test)  
y\_3 = m3.predict(x\_test)

In \[18\]:

\# 结果可视化  
plt.figure(figsize=(10, 6), dpi=100)

plt.scatter(x, y, label="data")  
plt.plot(x\_test, y\_1, label="max\_depth=1")  
plt.plot(x\_test, y\_2, label="max\_depth=3")  
plt.plot(x\_test, y\_3, label="linear regression")

plt.xlabel("数据")  
plt.ylabel("预测值")  
plt.legend()

plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 8][]

[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
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[20200227082352609.png]: https://img-blog.csdnimg.cn/20200227082352609.png
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