决策树算法(三)

其他 2020-03-07 22:46:32 阅读次数: 0

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决策树算法(一)

决策树算法(二)

决策树算法(三)

决策树算法(四)

4.6 案例：泰坦尼克号乘客生存预测

1 案例背景

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

案例：https://www.kaggle.com/c/titanic/overview

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

数据：http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt

经过观察数据得到:

  • 1 乘坐班是指乘客班（1，2，3），是社会经济阶层的代表。

  • 2 其中age数据存在缺失。

2 步骤分析

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

3 代码实现

  • 导入需要的模块
import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, export_graphviz
  • 1.获取数据
# 1、获取数据
titan = pd.read_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt")

  • 2.数据基本处理

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

  • 3.特征工程(字典特征抽取)

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

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

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

transfer = DictVectorizer(sparse=False)

x_train = transfer.fit_transform(x_train.to_dict(orient="records"))
x_test = transfer.fit_transform(x_test.to_dict(orient="records"))
  • 4.决策树模型训练和模型评估

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

# 4.机器学习(决策树)
estimator = DecisionTreeClassifier(criterion="entropy", max_depth=5)
estimator.fit(x_train, y_train)

# 5.模型评估
estimator.score(x_test, y_test)

estimator.predict(x_test)

决策树的结构是可以直接显示

4 决策树可视化

4.1 保存树的结构到dot文件

  • sklearn.tree.export_graphviz() 该函数能够导出DOT格式
    • tree.export_graphviz(estimator,out_file='tree.dot’,feature_names=[‘’,’’])
export_graphviz(estimator, out_file="./data/tree.dot", feature_names=['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', '女性', '男性'])

dot文件当中的内容如下

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

那么这个结构不能看清结构，所以可以在一个网站上显示

4.2 网站显示结构

5 决策树总结

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

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

6 小结

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

In [1]:

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

In [2]:

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

In [3]:

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

In [4]:

titan

Out[4]:

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

1313 rows × 11 columns

In [5]:

titan.describe()

Out[5]:

  row.names survived age
count 1313.000000 1313.000000 633.000000
mean 657.000000 0.341965 31.194181
std 379.174762 0.474549 14.747525
min 1.000000 0.000000 0.166700
25% 329.000000 0.000000 21.000000
50% 657.000000 0.000000 30.000000
75% 985.000000 1.000000 41.000000
max 1313.000000 1.000000 71.000000

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]:

  pclass age sex
0 1st 29.0000 female
1 1st 2.0000 female
2 1st 30.0000 male
3 1st 25.0000 female
4 1st 0.9167 male

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]:

  pclass age sex
0 1st 29.0000 female
1 1st 2.0000 female
2 1st 30.0000 male
3 1st 25.0000 female
4 1st 0.9167 male

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]:

  pclass age sex
649 3rd 45.000000 female
1078 3rd 31.194181 male
59 1st 31.194181 female
201 1st 18.000000 male
61 1st 31.194181 female

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'},
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 {'pclass': '2nd', 'age': 32.0, 'sex': 'male'},
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 {'pclass': '1st', 'age': 17.0, 'sex': 'female'},
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 {'pclass': '1st', 'age': 40.0, 'sex': 'male'},
 {'pclass': '1st', 'age': 4.0, 'sex': 'male'},
 {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '2nd', 'age': 29.0, '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': 22.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '1st', 'age': 57.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '1st', 'age': 40.0, 'sex': 'female'},
 {'pclass': '3rd', 'age': 47.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '1st', 'age': 37.0, 'sex': 'male'},
 {'pclass': '2nd', 'age': 42.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 21.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 20.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 5.0, 'sex': 'female'},
 {'pclass': '1st', 'age': 21.0, 'sex': 'female'},
 {'pclass': '3rd', 'age': 40.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 9.0, 'sex': 'female'},
 {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '1st', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '2nd', 'age': 41.0, 'sex': 'male'},
 {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '1st', 'age': 36.0, 'sex': 'female'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '2nd', 'age': 28.0, 'sex': 'male'},
 {'pclass': '1st', 'age': 39.0, 'sex': 'female'},
 {'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(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=15, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

In [19]:

# 5.模型评估
y_pre = estimator.predict(x_test)

In [20]:

y_pre

Out[20]:

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

In [21]:

ret = estimator.score(x_test, y_test)
print(ret)

0.779467680608365

In [22]:

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

In [23]:

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

In [26]:

# 通过超参数调优
from sklearn.model_selection import GridSearchCV

param = {"n_estimators":[100, 120, 300], "max_depth":[3, 7, 11]}

gc = GridSearchCV(rf, param_grid=param, cv=3)

gc.fit(x_train, y_train)

Out[26]:

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

In [27]:

print("随机森林预测结果是:\n", gc.score(x_test, y_test))

随机森林预测结果是:
 0.779467680608365
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