Chapter 12

第三章模型搭建和评估--建模

NotebookPython 353 cells

第三章模型搭建和评估--建模

经过前面的两章的知识点的学习，我可以对数数据的本身进行处理，比如数据本身的增删查补，还可以做必要的清洗工作。那么下面我们就要开始使用我们前面处理好的数据了。这一章我们要做的就是使用数据，我们做数据分析的目的也就是，运用我们的数据以及结合我的业务来得到某些我们需要知道的结果。那么分析的第一步就是建模，搭建一个预测模型或者其他模型；我们从这个模型的到结果之后，我们要分析我的模型是不是足够的可靠，那我就需要评估这个模型。今天我们学习建模，下一节我们学习评估。

我们拥有的泰坦尼克号的数据集，那么我们这次的目的就是，完成泰坦尼克号存活预测这个任务。

In [1]python · cell 4

python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import Image

In [2]python · cell 5

python

%matplotlib inline

In [3]python · cell 6

python

plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
plt.rcParams['figure.figsize'] = (10, 6)  # 设置输出图片大小

载入这些库，如果缺少某些库，请安装他们

【思考】这些库的作用是什么呢？你需要查一查

In [4]python · cell 8

python

#思考题回答

In [5]python · cell 9

python

%matplotlib inline

载入我们提供清洗之后的数据(clear_data.csv)，大家也将原始数据载入（train.csv），说说他们有什么不同

In [6]python · cell 11

python

plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
plt.rcParams['figure.figsize'] = (10, 6)  # 设置输出图片大小

In [7]python · cell 12

python

# 读取原数据数集
train = pd.read_csv('train.csv')
train.shape

Output

(891, 12)

In [8]python · cell 13

python

train.head()

Output

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   
4                           Allen, Mr. William Henry    male  35.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   NaN        S  
3      0            113803  53.1000  C123        S  
4      0            373450   8.0500   NaN        S

.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

In [9]python · cell 14

python

#读取清洗过的数据集
data = pd.read_csv('clear_data.csv')

In [10]python · cell 15

python

data.head()

Output

   PassengerId  Pclass   Age  SibSp  Parch     Fare  Sex_female  Sex_male  \
0            0       3  22.0      1      0   7.2500           0         1   
1            1       1  38.0      1      0  71.2833           1         0   
2            2       3  26.0      0      0   7.9250           1         0   
3            3       1  35.0      1      0  53.1000           1         0   
4            4       3  35.0      0      0   8.0500           0         1   

   Embarked_C  Embarked_Q  Embarked_S  
0           0           0           1  
1           1           0           0  
2           0           0           1  
3           0           0           1  
4           0           0           1

.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }

	PassengerId	Pclass	Age	SibSp	Fare	Sex_female	Sex_male	Embarked_C	Embarked_S
0	0	3	22.0	1	7.2500	0	1	0	1
1	1	1	38.0	1	71.2833	1	0	1	0
2	2	3	26.0	0	7.9250	1	0	0	1
3	3	1	35.0	1	53.1000	1	0	0	1
4	4	3	35.0	0	8.0500	0	1	0	1

模型搭建

处理完前面的数据我们就得到建模数据，下一步是选择合适模型
在进行模型选择之前我们需要先知道数据集最终是进行监督学习还是无监督学习
模型的选择一方面是通过我们的任务来决定的。
除了根据我们任务来选择模型外，还可以根据数据样本量以及特征的稀疏性来决定
刚开始我们总是先尝试使用一个基本的模型来作为其baseline，进而再训练其他模型做对比，最终选择泛化能力或性能比较好的模型

这里我的建模，并不是从零开始，自己一个人完成完成所有代码的编译。我们这里使用一个机器学习最常用的一个库（sklearn）来完成我们的模型的搭建

下面给出sklearn的算法选择路径，供大家参考

In [11]python · cell 20

python

# sklearn模型算法选择路径图
Image('sklearn.png')

Output

<IPython.core.display.Image object>

[省略较大 image/png 输出]

In [ ]python · cell 21

python

任务一：切割训练集和测试集

这里使用留出法划分数据集

将数据集分为自变量和因变量
按比例切割训练集和测试集(一般测试集的比例有30%、25%、20%、15%和10%)
使用分层抽样
设置随机种子以便结果能复现

【思考】

划分数据集的方法有哪些？
为什么使用分层抽样，这样的好处有什么？

任务提示1

切割数据集是为了后续能评估模型泛化能力
sklearn中切割数据集的方法为train_test_split
查看函数文档可以在jupyter noteboo里面使用train_test_split?后回车即可看到
分层和随机种子在参数里寻找

In [48]python · cell 26

python

from sklearn.model_selection import train_test_split

In [49]python · cell 27

python

# 一般先取出X和y后再切割，有些情况会使用到未切割的，这时候X和y就可以用,x是清洗好的数据，y是我们要预测的存活数据'Survived'
X = data
y = train['Survived']

In [50]python · cell 28

python

# 对数据集进行切割
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)

In [51]python · cell 29

python

# 查看数据形状
X_train.shape, X_test.shape

Output

((668, 11), (223, 11))

【思考】

什么情况下切割数据集的时候不用进行随机选取

In [52]python · cell 31

python

#思考回答

任务二：模型创建

创建基于线性模型的分类模型（逻辑回归）
创建基于树的分类模型（决策树、随机森林）
分别使用这些模型进行训练，分别的到训练集和测试集的得分
查看模型的参数，并更改参数值，观察模型变化

提示2

逻辑回归不是回归模型而是分类模型，不要与LinearRegression混淆
随机森林其实是决策树集成为了降低决策树过拟合的情况
线性模型所在的模块为sklearn.linear_model
树模型所在的模块为sklearn.ensemble

In [62]python · cell 34

python

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier

In [63]python · cell 35

python

# 默认参数逻辑回归模型
lr = LogisticRegression()
lr.fit(X_train, y_train)

Output

/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)

In [64]python · cell 36

python

# 查看训练集和测试集score值
print("Training set score: {:.2f}".format(lr.score(X_train, y_train)))
print("Testing set score: {:.2f}".format(lr.score(X_test, y_test)))

Output

Training set score: 0.80
Testing set score: 0.79

In [56]python · cell 37

python

# 调整参数后的逻辑回归模型
lr2 = LogisticRegression(C=100)
lr2.fit(X_train, y_train)

Output

/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)

LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)

In [57]python · cell 38

python

print("Training set score: {:.2f}".format(lr2.score(X_train, y_train)))
print("Testing set score: {:.2f}".format(lr2.score(X_test, y_test)))

Output

Training set score: 0.79
Testing set score: 0.78

In [58]python · cell 39

python

# 默认参数的随机森林分类模型
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

Output

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)

In [59]python · cell 40

python

print("Training set score: {:.2f}".format(rfc.score(X_train, y_train)))
print("Testing set score: {:.2f}".format(rfc.score(X_test, y_test)))

Output

Training set score: 1.00
Testing set score: 0.83

In [60]python · cell 41

python

# 调整参数后的随机森林分类模型
rfc2 = RandomForestClassifier(n_estimators=100, max_depth=5)
rfc2.fit(X_train, y_train)

Output

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=5, 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)

In [61]python · cell 42

python

print("Training set score: {:.2f}".format(rfc2.score(X_train, y_train)))
print("Testing set score: {:.2f}".format(rfc2.score(X_test, y_test)))

Output

Training set score: 0.86
Testing set score: 0.81

【思考】

为什么线性模型可以进行分类任务，背后是怎么的数学关系
对于多分类问题，线性模型是怎么进行分类的

In [69]python · cell 44

python

#思考回答

任务三：输出模型预测结果

输出模型预测分类标签
输出不同分类标签的预测概率

提示3

一般监督模型在sklearn里面有个predict能输出预测标签，predict_proba则可以输出标签概率

In [65]python · cell 47

python

# 预测标签
pred = lr.predict(X_train)

In [66]python · cell 48

python

# 此时我们可以看到0和1的数组
pred[:10]

Output

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

In [67]python · cell 49

python

# 预测标签概率
pred_proba = lr.predict_proba(X_train)

In [68]python · cell 50

python

pred_proba[:10]

Output

array([[0.60870022, 0.39129978],
       [0.17725433, 0.82274567],
       [0.40750365, 0.59249635],
       [0.18925851, 0.81074149],
       [0.87973912, 0.12026088],
       [0.91374559, 0.08625441],
       [0.13293198, 0.86706802],
       [0.90560801, 0.09439199],
       [0.05283987, 0.94716013],
       [0.10936016, 0.89063984]])

【思考】

预测标签的概率对我们有什么帮助

In [73]python · cell 52

python

#思考回答

In [ ]python · cell 53

python

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.rcParams['figure.figsize'] = (10, 6) # 设置输出图片大小

PassengerId Survived Pclass \ 0 1 0 3 1 2 1 1 2 3 1 3 3 4 1 1 4 5 0 3 Name Sex Age SibSp \ 0 Braund, Mr. Owen Harris male 22.0 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 2 Heikkinen, Miss. Laina female 26.0 0 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 4 Allen, Mr. William Henry male 35.0 0 Parch Ticket Fare Cabin Embarked 0 0 A/5 21171 7.2500 NaN S 1 0 PC 17599 71.2833 C85 C 2 0 STON/O2. 3101282 7.9250 NaN S 3 0 113803 53.1000 C123 S 4 0 373450 8.0500 NaN S

PassengerId

Survived

Pclass

Name

Sex

Age

SibSp

Parch

Ticket

Fare

Cabin

Embarked

Braund, Mr. Owen Harris

male

22.0

A/5 21171

7.2500

NaN

Cumings, Mrs. John Bradley (Florence Briggs Th...

female

38.0

PC 17599

71.2833

C85

Heikkinen, Miss. Laina

female

26.0

STON/O2. 3101282

7.9250

NaN

Futrelle, Mrs. Jacques Heath (Lily May Peel)

female

35.0

113803

53.1000

C123

Allen, Mr. William Henry

male

35.0

373450

8.0500

NaN

PassengerId Pclass Age SibSp Parch Fare Sex_female Sex_male \ 0 0 3 22.0 1 0 7.2500 0 1 1 1 1 38.0 1 0 71.2833 1 0 2 2 3 26.0 0 0 7.9250 1 0 3 3 1 35.0 1 0 53.1000 1 0 4 4 3 35.0 0 0 8.0500 0 1 Embarked_C Embarked_Q Embarked_S 0 0 0 1 1 1 0 0 2 0 0 1 3 0 0 1 4 0 0 1

PassengerId

Pclass

Age

SibSp

Parch

Fare

Sex_female

Sex_male

Embarked_C

Embarked_Q

Embarked_S

22.0

7.2500

38.0

71.2833

26.0

7.9250

35.0

53.1000

35.0

8.0500

# 一般先取出X和y后再切割，有些情况会使用到未切割的，这时候X和y就可以用,x是清洗好的数据，y是我们要预测的存活数据'Survived' X = data y = train['Survived']

/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=100, multi_class='auto', n_jobs=None, penalty='l2', random_state=None, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False)

LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, l1_ratio=None, max_iter=100, multi_class='auto', n_jobs=None, penalty='l2', random_state=None, solver='lbfgs', tol=0.0001, verbose=0, warm_start=False)

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)

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=5, 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)

array([[0.60870022, 0.39129978], [0.17725433, 0.82274567], [0.40750365, 0.59249635], [0.18925851, 0.81074149], [0.87973912, 0.12026088], [0.91374559, 0.08625441], [0.13293198, 0.86706802], [0.90560801, 0.09439199], [0.05283987, 0.94716013], [0.10936016, 0.89063984]])