决策树算法(四)

其他 2020-03-07 22:46:18 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)

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]:

  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 [6]:

titan.describe()

Out[6]:

  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 [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]:

  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 [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]:

  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 [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]:

  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 [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'},
 {'pclass': '3rd', 'age': 18.0, 'sex': 'male'},
 {'pclass': '1st', 'age': 48.0, 'sex': 'female'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '2nd', 'age': 50.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 18.0, 'sex': 'female'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'female'},
 {'pclass': '3rd', 'age': 34.0, 'sex': 'male'},
 {'pclass': '2nd', 'age': 21.0, 'sex': 'female'},
 {'pclass': '2nd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '1st', 'age': 44.0, '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': 19.0, 'sex': 'male'},
 {'pclass': '1st', 'age': 39.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
 {'pclass': '1st', 'age': 42.0, 'sex': 'male'},
 {'pclass': '3rd', 'age': 31.19418104265403, 'sex': 'male'},
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 {'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。

2.算法描述

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。

x 1 2 3 4 5 6 7 8 9 10
y 5.56 5.7 5.91 6.4 6.8 7.05 8.9 8.7 9 9.05

同理，得到其他各切分点的子区域输出值，如下表：

s 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5
c1 5.56 5.63 5.72 5.89 6.07 6.24 6.62 6.88 7.11
c2 7.5 7.73 7.99 8.25 8.54 8.91 8.92 9.03 9.05

同理，计算得到其他各切分点的损失函数值，可获得下表：

s 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5
m(s) 15.72 12.07 8.36 5.78 3.91 1.93 8.01 11.73 15.74

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】

（3）调用步骤 (1)、(2)，继续划分：

对R1继续进行划分:

x 1 2 3 4 5 6
y 5.56 5.7 5.91 6.4 6.8 7.05

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：

s 1.5 2.5 3.5 4.5 5.5
c1 5.56 5.63 5.72 5.89 6.07
c2 6.37 6.54 6.75 6.93 7.05

计算损失函数值m(s)：

s 1.5 2.5 3.5 4.5 5.5
m(s) 1.3087 0.754 0.2771 0.4368 1.0644

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：

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

4 小结

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]:

x

Out[8]:

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

In [10]:

y

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()

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