Python 第3次作业

1. 载入分析所需要的库和模块

import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import matplotlib.pyplot as plt
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)

from sklearn import metrics
from sklearn.metrics import RocCurveDisplay
from sklearn.metrics import cohen_kappa_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

2. 数据读取及观察

使用 pandas 库中的 read_csv 函数读取 CSV 文件

data = pd.read_csv('../data/数据5.2.csv')

data.info() 是 Pandas 库中 DataFrame 对象的一个方法，用于打印 DataFrame 的简明摘要信息。该方法会输出以下内容：

  1. 数据的基本信息，包含索引范围和数据的总行数。
  2. 每列的信息，包括列名、非空值数量、数据类型。
  3. 内存使用情况，即该 DataFrame 占用的内存大小。
# 查看数据集的基本信息
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 700 entries, 0 to 699
Data columns (total 7 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   V1      700 non-null    int64  
 1   V2      700 non-null    float64
 2   V3      700 non-null    int64  
 3   V4      700 non-null    int64  
 4   V5      700 non-null    float64
 5   V6      700 non-null    int64  
 6   V7      700 non-null    int64  
dtypes: float64(2), int64(5)
memory usage: 38.4 KB

对数据集的基本信息分析如下：

所有列均为非空（Non-Null Count 均为 700），说明数据没有缺失值。

# 查看有哪些列
data.columns

Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7'], dtype='object')

# 查看数据形状
data.shape

(700, 7)

# 查看数据类型
data.dtypes

V1      int64
V2    float64
V3      int64
V4      int64
V5    float64
V6      int64
V7      int64
dtype: object

# 查看是否有缺失值
data.isnull().values.any() 

False

# 查看缺失值数量
data.isnull().sum() 

V1    0
V2    0
V3    0
V4    0
V5    0
V6    0
V7    0
dtype: int64

# 查看前五行数据
data.head()
V1 V2 V3 V4 V5 V6 V7
0 0 20.33 3 1 20.66 0 0
1 0 36.59 1 1 8.67 0 1
2 0 34.96 2 1 19.67 1 0
3 0 26.83 1 2 21.54 1 1
4 0 21.14 4 1 16.92 0 1

3. 描述性分析

3.1 计算统计各变量的统计指标

针对数据集中各变量计算平均值、标准差、最大值、最小值、四分位数等统计指标，针对连续变量的结果进行解读；

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
data.describe()
V1 V2 V3 V4 V5 V6 V7
count 700.000000 700.000000 700.000000 700.000000 700.000000 700.000000 700.000000
mean 0.261429 28.341543 2.041429 1.501429 12.586629 0.535714 0.445714
std 0.439727 6.501841 0.947702 1.173746 7.509957 0.499079 0.497400
min 0.000000 16.260000 1.000000 1.000000 1.740000 0.000000 0.000000
25% 0.000000 23.580000 1.000000 1.000000 6.800000 0.000000 0.000000
50% 0.000000 27.640000 2.000000 1.000000 10.760000 1.000000 0.000000
75% 1.000000 32.520000 2.000000 1.000000 16.837500 1.000000 1.000000
max 1.000000 45.530000 5.000000 5.000000 46.730000 1.000000 1.000000

数据集中的连续变量为V2和V5，对其分析如下：

  1. V2：数值较大，取值范围广，数据在均值周围有一定波动，分布较为连续且逐步递增。
  2. V5：取值范围非常大，数据离散程度很高，分布较为分散。

3.2 按照V1变量的取值分组对其他变量开展描述性分析

针对连续变量，通常使用计算平均值、标准差、最大值、最小值、四分位数等统计指标的方式来进行描述性分析；针对分类变量，通常使用交叉表的方式开展分析。

# 按照 V1 变量的取值分组对其他变量开展描述性分析
grouped = data.groupby('V1')
data.groupby('V1').describe().unstack()

           V1
V2  count  0     517.000000
           1     183.000000
    mean   0      28.873694
           1      26.838142
    std    0       6.266412
           1       6.924717
    min    0      16.260000
           1      16.260000
    25%    0      23.580000
           1      21.950000
    50%    0      28.460000
           1      25.200000
    75%    0      33.330000
           1      31.710000
    max    0      45.530000
           1      44.720000
V3  count  0     517.000000
           1     183.000000
    mean   0       2.021277
           1       2.098361
    std    0       0.909432
           1       1.048886
    min    0       1.000000
           1       1.000000
    25%    0       1.000000
           1       1.000000
    50%    0       2.000000
           1       2.000000
    75%    0       2.000000
           1       2.000000
    max    0       5.000000
           1       5.000000
V4  count  0     517.000000
           1     183.000000
    mean   0       1.460348
           1       1.617486
    std    0       1.119279
           1       1.311873
    min    0       1.000000
           1       1.000000
    25%    0       1.000000
           1       1.000000
    50%    0       1.000000
           1       1.000000
    75%    0       1.000000
           1       1.000000
    max    0       5.000000
           1       5.000000
V5  count  0     517.000000
           1     183.000000
    mean   0      10.847234
           1      17.500656
    std    0       6.176717
           1       8.693078
    min    0       1.740000
           1       2.290000
    25%    0       6.250000
           1      10.705000
    50%    0       9.330000
           1      16.480000
    75%    0      14.280000
           1      22.585000
    max    0      37.050000
           1      46.730000
V6  count  0     517.000000
           1     183.000000
    mean   0       0.541586
           1       0.519126
    std    0       0.498750
           1       0.501005
    min    0       0.000000
           1       0.000000
    25%    0       0.000000
           1       0.000000
    50%    0       1.000000
           1       1.000000
    75%    0       1.000000
           1       1.000000
    max    0       1.000000
           1       1.000000
V7  count  0     517.000000
           1     183.000000
    mean   0       0.433269
           1       0.480874
    std    0       0.496007
           1       0.501005
    min    0       0.000000
           1       0.000000
    25%    0       0.000000
           1       0.000000
    50%    0       0.000000
           1       0.000000
    75%    0       1.000000
           1       1.000000
    max    0       1.000000
           1       1.000000
dtype: float64

从 count 行数据可知，两组数据的样本量不同，0 组样本量为 517，1 组样本量为 183。

变量 分析维度 V1=0 的数据 V1=1 的数据 分析结论
V2 均值 28.873694 26.838142 0 组均值略高于 1 组
V2 标准差 6.266412 6.924717 1 组数据相对更分散
V2 最值 最小值：16.260000，最大值略高 最小值：16.260000，最大值略低 两组最小值相同，0 组最大值略高于 1 组
V3 均值 2.021277 2.098361 两组均值都接近 2
V3 分布 大部分集中在 1 - 2 之间 大部分集中在 1 - 2 之间 从 25% - 75% 分位数来看，两组数据分布较为集中
V4 分布集中 大部分分位数（25% - 75%）的值为 1 大部分分位数（25% - 75%）的值为 1 数据集中在 1 附近
V4 均值 低于 1 组 略高于 0 组 1 组均值略高于 0 组
V5 均值差异 10.847234 17.500656 1 组均值明显高于 0 组
V5 离散程度 6.176717 8.693078 1 组数据更分散
V6 和 V7 二值特征 取值范围 0 - 1 取值范围 0 - 1 推测可能是二值特征
V6 和 V7 分布 分位数有较多重合，标准差接近 0.5 分位数有较多重合，标准差接近 0.5 数据在 0 和 1 上分布较为均衡

3.3 对V1和V3变量进行交叉表分析

交叉表分析是描述统计的一种，分析特色是将数据按照行变量、列变量进行描述统计。在 Pandas 里，pd.crosstab 函数可用于创建交叉表，用于展示两个或多个分类变量之间的频数分布。

# 使用交叉表分析 “V1是否购买本次推广产品” 和 “V3年收入水平”
pd.crosstab(data.V3, data.V1)
V1 0 1
V3
1 139 59
2 293 79
3 24 14
4 57 30
5 4 1

样本客户主要集中在V3 级别 1 和 级别 2，表明推广活动主要触达的是这两个收入区间的群体。

  1. 级别 2 的人数最多（372 人，占总数的 ≈53.1%）。
  2. 级别 1 其次（198 人，占总数的 ≈28.3%）。
  3. 级别 5 的样本量极小（仅 5 人）。

为了衡量收入水平对购买决策的影响，可以计算每个收入级别的购买率（即已购买人数占该级别总人数的比例）。

V3 (收入级别) 总人数 购买人数 (V1=1) 购买率 (转化率)
1 198 59 59/198≈29.8%
2 372 79 79/372≈21.2%
3 38 14 14/38≈36.8%
4 87 30 30/87≈34.5%
5 5 1 1/5=20.0% 
# 查看“V3年收入水平”和“V1是否购买本次推广产品”的交叉表， normalize='index' 表示按行归一化
pd.crosstab(data.V3, data.V1, normalize='index')
V1 0 1
V3
1 0.702020 0.297980
2 0.787634 0.212366
3 0.631579 0.368421
4 0.655172 0.344828
5 0.800000 0.200000

4. 数据处理

区分分类特征和连续特征并进行处理，对分类特征设置虚拟变量，对连续特征进行标准化处理；

def data_encoding(data):
    # 选取指定列的数据
    data = data[["V1", "V2", "V3", "V4", "V5", "V6", "V7"]]

    # 定义离散特征和连续特征
    discrete_feature = ["V3"]
    continuous_feature = ["V2", "V5", "V4", "V6", "V7"]

    # 对离散特征进行独热编码
    df = pd.get_dummies(data, columns=discrete_feature)
    
    # 对连续特征进行标准化处理
    df[continuous_feature] = (df[continuous_feature] - df[continuous_feature].mean()) / df[continuous_feature].std()
    
    # 将 V1 列添加回处理后的数据框
    df["V1"] = data[["V1"]]

    return df

data = data_encoding(data)
data
V1 V2 V4 V5 V6 V7 V3_1 V3_2 V3_3 V3_4 V3_5
0 0 -1.232196 -0.427204 1.075022 -1.073405 -0.896089 False False True False False
1 0 1.268634 -0.427204 -0.521525 -1.073405 1.114367 True False False False False
2 0 1.017936 -0.427204 0.943197 0.930284 -0.896089 False True False False False
3 0 -0.232479 0.424769 1.192200 0.930284 1.114367 True False False False False
4 0 -1.107616 -0.427204 0.577017 -1.073405 1.114367 False False False True False
5 0 1.268634 -0.427204 -1.195297 0.930284 -0.896089 False True False False False
6 0 -0.607758 -0.427204 0.035070 0.930284 -0.896089 False True False False False
7 0 -0.983036 -0.427204 -0.624055 0.930284 1.114367 False False False True False
8 0 -1.232196 0.424769 0.606311 0.930284 -0.896089 False True False False False
9 0 -1.232196 -0.427204 -1.078119 0.930284 -0.896089 False True False False False
10 0 -1.107616 -0.427204 2.305389 -1.073405 1.114367 True False False False False
11 0 -0.607758 -0.427204 0.855314 -1.073405 -0.896089 True False False False False
12 0 -0.357059 -0.427204 0.606311 0.930284 -0.896089 True False False False False
13 1 -0.858456 0.424769 1.001786 0.930284 1.114367 True False False False False
14 0 1.268634 -0.427204 -0.887705 -1.073405 1.114367 False True False False False
15 0 -1.482894 -0.427204 -0.536172 0.930284 -0.896089 False True False False False
16 0 -0.107899 -0.427204 -0.331111 0.930284 1.114367 False True False False False
17 0 0.893356 -0.427204 -0.829116 0.930284 -0.896089 True False False False False
18 0 0.518077 -0.427204 0.415897 -1.073405 1.114367 False True False False False
19 1 -1.107616 -0.427204 -0.404347 -1.073405 -0.896089 False True False False False
20 0 -1.733593 -0.427204 -0.506878 0.930284 1.114367 True False False False False
21 0 0.018219 0.424769 -0.843764 -1.073405 -0.896089 False True False False False
22 0 1.517794 -0.427204 0.020422 -1.073405 1.114367 False True False False False
23 1 -1.482894 -0.427204 0.166895 -1.073405 1.114367 True False False False False
24 0 0.018219 -0.427204 -0.418994 0.930284 -0.896089 False True False False False
25 1 -0.107899 -0.427204 0.342661 0.930284 -0.896089 False True False False False
26 0 1.393214 -0.427204 0.547722 0.930284 -0.896089 False True False False False
27 1 1.393214 -0.427204 0.386603 0.930284 1.114367 False True False False False
28 0 0.391959 -0.427204 -0.960941 -1.073405 -0.896089 False False False True False
29 0 0.642658 -0.427204 -0.111403 -1.073405 -0.896089 True False False False False
30 1 0.267379 -0.427204 0.708842 0.930284 1.114367 False True False False False
31 0 0.018219 0.424769 -0.770528 -1.073405 -0.896089 False True False False False
32 0 0.518077 -0.427204 -1.151355 -1.073405 -0.896089 True False False False False
33 0 0.642658 -0.427204 -1.136708 0.930284 1.114367 False False False True False
34 0 1.893073 -0.427204 0.284072 -1.073405 -0.896089 False False False True False
35 0 -0.483177 0.424769 -1.151355 0.930284 1.114367 False True False False False
36 1 -0.732338 -0.427204 -0.418994 -1.073405 -0.896089 False True False False False
37 0 0.267379 -0.427204 0.313367 0.930284 1.114367 False True False False False
38 0 -0.232479 -0.427204 -0.477583 -1.073405 1.114367 True False False False False
39 0 -1.107616 -0.427204 -0.360405 -1.073405 -0.896089 True False False False False
40 1 -0.357059 -0.427204 1.909914 -1.073405 -0.896089 True False False False False
41 1 -0.858456 -0.427204 -0.565466 0.930284 -0.896089 True False False False False
42 0 0.518077 -0.427204 -0.169991 0.930284 -0.896089 False True False False False
43 1 -0.858456 -0.427204 -0.301816 0.930284 -0.896089 True False False False False
44 1 -0.732338 -0.427204 1.221495 -1.073405 -0.896089 False True False False False
45 0 -0.107899 -0.427204 1.880619 0.930284 -0.896089 True False False False False
46 0 -0.858456 -0.427204 1.983150 0.930284 -0.896089 True False False False False
47 0 1.517794 -0.427204 0.371956 0.930284 -0.896089 False True False False False
48 0 1.017936 1.276742 0.049717 -1.073405 1.114367 False False True False False
49 0 0.767238 -0.427204 -1.253886 0.930284 -0.896089 False True False False False
50 0 1.517794 -0.427204 1.983150 0.930284 -0.896089 False True False False False
51 0 -1.358314 -0.427204 -1.415005 -1.073405 1.114367 False True False False False
52 0 -0.732338 -0.427204 -0.521525 0.930284 1.114367 False True False False False
53 0 -0.232479 -0.427204 -1.122061 -1.073405 -0.896089 False True False False False
54 0 0.142799 -0.427204 -1.034178 0.930284 1.114367 False True False False False
55 1 -1.107616 -0.427204 0.035070 0.930284 -0.896089 False True False False False
56 0 -0.483177 -0.427204 0.987139 0.930284 -0.896089 False True False False False
57 0 0.267379 -0.427204 1.367967 -1.073405 -0.896089 False True False False False
58 0 0.018219 -0.427204 0.108306 -1.073405 1.114367 False False True False False
59 0 -1.107616 -0.427204 -0.711939 0.930284 -0.896089 False True False False False
60 0 0.642658 -0.427204 1.250789 -1.073405 -0.896089 False True False False False
61 1 -0.732338 -0.427204 0.313367 -1.073405 1.114367 True False False False False
62 0 0.267379 1.276742 2.100328 -1.073405 1.114367 False False False True False
63 0 -0.357059 -0.427204 -1.078119 -1.073405 1.114367 False True False False False
64 1 -1.482894 -0.427204 2.554392 -1.073405 1.114367 True False False False False
65 0 -0.357059 -0.427204 -0.594761 0.930284 -0.896089 False True False False False
66 1 -1.232196 1.276742 0.166895 -1.073405 -0.896089 False True False False False
67 0 0.391959 -0.427204 0.049717 0.930284 -0.896089 False True False False False
68 1 -0.357059 -0.427204 -0.653350 0.930284 1.114367 True False False False False
69 1 0.767238 -0.427204 2.349331 -1.073405 1.114367 False False False True False
70 0 0.142799 -0.427204 -0.858411 0.930284 -0.896089 False True False False False
71 0 0.893356 -0.427204 -1.019530 0.930284 -0.896089 False False False True False
72 0 0.767238 -0.427204 -0.433641 0.930284 1.114367 False True False False False
73 0 -0.732338 -0.427204 -0.580114 0.930284 -0.896089 True False False False False
74 1 0.391959 -0.427204 -0.140697 -1.073405 1.114367 False False False True False
75 0 0.518077 -0.427204 -1.122061 -1.073405 1.114367 False True False False False
76 0 0.142799 1.276742 -0.287169 0.930284 -0.896089 False True False False False
77 0 -0.232479 -0.427204 -0.653350 -1.073405 1.114367 False True False False False
78 0 0.893356 -0.427204 -1.048825 -1.073405 1.114367 True False False False False
79 0 0.767238 -0.427204 1.953856 0.930284 -0.896089 True False False False False
80 0 0.642658 -0.427204 -0.975589 -1.073405 -0.896089 False True False False False
81 1 2.268351 -0.427204 0.137600 -1.073405 1.114367 False True False False False
82 0 -0.858456 -0.427204 0.474486 -1.073405 -0.896089 False True False False False
83 0 0.142799 -0.427204 -0.345758 -1.073405 -0.896089 True False False False False
84 0 -0.107899 -0.427204 -0.023519 -1.073405 -0.896089 True False False False False
85 0 1.268634 -0.427204 -0.785175 0.930284 -0.896089 False False False True False
86 0 0.391959 -0.427204 -0.199286 0.930284 -0.896089 False True False False False
87 0 -1.733593 -0.427204 -0.375053 0.930284 1.114367 False False False True False
88 0 0.893356 2.128715 1.631617 0.930284 -0.896089 False True False False False
89 0 2.143771 -0.427204 -0.243228 0.930284 -0.896089 False True False False False
90 0 -1.232196 -0.427204 -0.741233 0.930284 1.114367 True False False False False
91 0 -0.107899 2.128715 -0.594761 -1.073405 -0.896089 True False False False False
92 0 0.642658 -0.427204 -1.004883 0.930284 -0.896089 True False False False False
93 1 -0.483177 -0.427204 0.371956 -1.073405 -0.896089 True False False False False
94 1 -1.232196 -0.427204 -0.521525 0.930284 1.114367 False True False False False
95 0 1.268634 -0.427204 -0.111403 -1.073405 1.114367 False True False False False
96 1 -1.482894 -0.427204 -0.272522 -1.073405 -0.896089 False True False False False
97 1 -0.107899 -0.427204 3.667580 0.930284 1.114367 False False True False False
98 1 -0.483177 -0.427204 0.357308 0.930284 -0.896089 False True False False False
99 0 -0.858456 -0.427204 -1.092766 0.930284 1.114367 False True False False False
100 0 0.767238 -0.427204 -0.858411 -1.073405 -0.896089 False True False False False
101 1 0.518077 -0.427204 0.401250 -1.073405 -0.896089 True False False False False
102 0 -0.732338 -0.427204 -1.297827 -1.073405 -0.896089 True False False False False
103 0 -1.733593 2.980688 -0.184639 -1.073405 -0.896089 False True False False False
104 0 0.767238 -0.427204 -0.653350 0.930284 -0.896089 True False False False False
105 0 -1.107616 -0.427204 -1.078119 -1.073405 -0.896089 False False True False False
106 0 -0.232479 -0.427204 0.386603 0.930284 -0.896089 False True False False False
107 0 1.268634 -0.427204 1.045728 0.930284 1.114367 True False False False False
108 0 0.518077 2.128715 0.503781 -1.073405 -0.896089 False True False False False
109 0 -1.358314 -0.427204 -0.580114 -1.073405 1.114367 False True False False False
110 0 -0.732338 -0.427204 -1.180650 0.930284 -0.896089 True False False False False
111 0 -0.983036 -0.427204 0.313367 -1.073405 -0.896089 True False False False False
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591 0 1.268634 -0.427204 -0.711939 -1.073405 1.114367 True False False False False
592 0 1.642374 -0.427204 -0.448289 -1.073405 1.114367 True False False False False
593 0 0.391959 -0.427204 -0.477583 0.930284 -0.896089 False True False False False
594 0 -0.732338 -0.427204 -0.711939 0.930284 -0.896089 False True False False False
595 0 0.893356 -0.427204 -0.975589 -1.073405 -0.896089 False False True False False
596 0 -0.483177 -0.427204 0.093659 0.930284 1.114367 False True False False False
597 0 0.642658 -0.427204 -0.492230 0.930284 1.114367 False True False False False
598 0 -1.107616 -0.427204 1.397261 -1.073405 1.114367 False True False False False
599 0 -1.232196 -0.427204 1.455850 0.930284 -0.896089 False True False False False
600 0 2.268351 -0.427204 -0.345758 0.930284 1.114367 False False True False False
601 0 1.893073 -0.427204 0.562370 -1.073405 -0.896089 False False False True False
602 1 1.517794 -0.427204 1.880619 -1.073405 1.114367 False True False False False
603 1 -1.482894 -0.427204 -0.272522 -1.073405 1.114367 False True False False False
604 0 0.267379 -0.427204 -0.902353 -1.073405 -0.896089 False False False True False
605 0 0.767238 -0.427204 0.181542 -1.073405 -0.896089 False True False False False
606 1 -0.858456 -0.427204 0.445192 -1.073405 -0.896089 True False False False False
607 1 -0.483177 -0.427204 2.993808 -1.073405 1.114367 False True False False False
608 0 0.767238 -0.427204 -0.580114 0.930284 1.114367 True False False False False
609 0 0.142799 -0.427204 -0.052814 0.930284 -0.896089 False True False False False
610 0 0.767238 -0.427204 -0.858411 -1.073405 1.114367 False True False False False
611 0 2.268351 -0.427204 -0.682644 0.930284 -0.896089 True False False False False
612 0 -0.483177 -0.427204 0.181542 -1.073405 1.114367 True False False False False
613 0 -0.858456 -0.427204 -1.078119 -1.073405 -0.896089 True False False False False
614 0 1.642374 -0.427204 0.254778 0.930284 1.114367 False True False False False
615 1 -1.482894 -0.427204 -0.931647 0.930284 -0.896089 True False False False False
616 0 -0.483177 -0.427204 -0.506878 -1.073405 -0.896089 False True False False False
617 0 -0.983036 -0.427204 -0.785175 0.930284 1.114367 False True False False False
618 0 1.017936 -0.427204 -1.312475 0.930284 1.114367 False True False False False
619 0 -0.732338 -0.427204 1.236142 -1.073405 1.114367 True False False False False
620 1 2.268351 -0.427204 -0.565466 0.930284 1.114367 False True False False False
621 0 0.391959 -0.427204 0.489133 -1.073405 -0.896089 False True False False False
622 0 -0.607758 -0.427204 -0.257875 0.930284 1.114367 False True False False False
623 1 -0.858456 -0.427204 -0.126050 -1.073405 1.114367 False False False True False
624 0 0.142799 -0.427204 3.257458 -1.073405 -0.896089 False True False False False
625 0 1.642374 -0.427204 -1.195297 -1.073405 1.114367 False True False False False
626 0 -0.483177 -0.427204 -0.536172 0.930284 -0.896089 False True False False False
627 0 0.018219 -0.427204 0.035070 -1.073405 1.114367 False True False False False
628 0 1.893073 -0.427204 0.328014 0.930284 1.114367 False True False False False
629 0 0.893356 -0.427204 0.694195 0.930284 -0.896089 True False False False False
630 0 0.642658 -0.427204 -0.462936 0.930284 -0.896089 False True False False False
631 1 0.267379 -0.427204 -1.371064 0.930284 1.114367 True False False False False
632 0 -0.483177 -0.427204 -1.092766 -1.073405 1.114367 False False False True False
633 0 0.391959 -0.427204 -0.331111 0.930284 -0.896089 False True False False False
634 0 0.018219 -0.427204 0.049717 0.930284 -0.896089 False True False False False
635 0 1.142516 -0.427204 -0.682644 0.930284 1.114367 False True False False False
636 0 1.142516 -0.427204 0.430545 0.930284 1.114367 True False False False False
637 0 -0.232479 -0.427204 -1.371064 -1.073405 -0.896089 False True False False False
638 0 0.018219 -0.427204 -0.785175 -1.073405 -0.896089 False True False False False
639 0 0.142799 -0.427204 0.401250 -1.073405 -0.896089 False False False True False
640 0 -1.482894 -0.427204 -0.580114 -1.073405 1.114367 True False False False False
641 0 -0.983036 -0.427204 0.371956 -1.073405 -0.896089 False True False False False
642 0 -0.107899 -0.427204 -1.327122 -1.073405 -0.896089 False True False False False
643 1 0.018219 -0.427204 0.122953 -1.073405 1.114367 False True False False False
644 0 -1.358314 -0.427204 -0.960941 0.930284 -0.896089 True False False False False
645 0 1.642374 -0.427204 -0.565466 -1.073405 1.114367 False True False False False
646 0 -1.107616 -0.427204 0.225484 0.930284 1.114367 True False False False False
647 0 0.518077 -0.427204 1.236142 -1.073405 1.114367 True False False False False
648 0 -1.358314 -0.427204 -0.667997 0.930284 1.114367 True False False False False
649 0 -0.983036 -0.427204 -1.341769 -1.073405 -0.896089 False True False False False
650 0 0.642658 -0.427204 -0.536172 0.930284 1.114367 False False False True False
651 0 -0.357059 -0.427204 -0.960941 0.930284 1.114367 True False False False False
652 0 0.518077 -0.427204 -0.960941 0.930284 1.114367 True False False False False
653 0 0.642658 -0.427204 0.635606 -1.073405 1.114367 False True False False False
654 0 -0.357059 -0.427204 -0.169991 -1.073405 1.114367 False True False False False
655 0 0.518077 -0.427204 1.265436 -1.073405 -0.896089 False True False False False
656 1 -1.358314 -0.427204 2.730158 0.930284 -0.896089 False True False False False
657 1 1.017936 -0.427204 -0.550819 -1.073405 -0.896089 False False True False False
658 0 2.643629 -0.427204 0.694195 0.930284 1.114367 False True False False False
659 0 -0.357059 -0.427204 -0.843764 0.930284 -0.896089 False False False True False
660 0 0.893356 -0.427204 -0.008872 -1.073405 1.114367 False True False False False
661 1 1.393214 -0.427204 0.430545 -1.073405 1.114367 False False False True False
662 1 -0.483177 -0.427204 0.899256 -1.073405 -0.896089 False True False False False
663 0 1.017936 -0.427204 -1.122061 -1.073405 1.114367 False True False False False
664 0 -0.858456 -0.427204 0.181542 0.930284 1.114367 True False False False False
665 0 0.518077 -0.427204 0.386603 0.930284 -0.896089 False False False True False
666 0 0.518077 -0.427204 -0.345758 0.930284 -0.896089 False True False False False
667 1 0.267379 -0.427204 0.855314 -1.073405 -0.896089 False False True False False
668 0 -0.607758 -0.427204 0.254778 -1.073405 -0.896089 False True False False False
669 0 0.267379 -0.427204 -0.638703 0.930284 -0.896089 False False False False True
670 0 -1.607474 -0.427204 1.060375 0.930284 1.114367 False True False False False
671 0 1.893073 -0.427204 0.591664 -1.073405 1.114367 False True False False False
672 0 -0.607758 -0.427204 -0.858411 0.930284 -0.896089 False True False False False
673 0 0.518077 -0.427204 -0.917000 0.930284 1.114367 False True False False False
674 1 -1.733593 -0.427204 0.328014 0.930284 1.114367 True False False False False
675 0 -0.483177 -0.427204 -1.122061 0.930284 -0.896089 False False False True False
676 0 -0.483177 -0.427204 -0.653350 -1.073405 1.114367 True False False False False
677 1 0.642658 -0.427204 -1.195297 -1.073405 1.114367 False True False False False
678 0 -0.107899 -0.427204 -0.140697 -1.073405 -0.896089 False True False False False
679 0 0.018219 -0.427204 -1.063472 -1.073405 -0.896089 False True False False False
680 1 -0.607758 -0.427204 2.217506 0.930284 1.114367 False True False False False
681 1 -1.858173 -0.427204 -0.082108 -1.073405 -0.896089 False True False False False
682 0 0.142799 -0.427204 0.181542 0.930284 1.114367 False False True False False
683 0 -1.733593 -0.427204 -0.580114 0.930284 1.114367 False True False False False
684 0 -0.107899 -0.427204 0.035070 0.930284 1.114367 False True False False False
685 1 0.018219 -0.427204 0.855314 -1.073405 -0.896089 False True False False False
686 0 0.018219 -0.427204 -0.345758 -1.073405 1.114367 False False False True False
687 1 -0.107899 -0.427204 0.005775 -1.073405 -0.896089 False True False False False
688 0 -0.232479 -0.427204 0.079011 -1.073405 -0.896089 False True False False False
689 0 -0.607758 -0.427204 0.064364 0.930284 -0.896089 False True False False False
690 0 -1.358314 -0.427204 -0.433641 0.930284 -0.896089 True False False False False
691 0 1.517794 -0.427204 -0.448289 0.930284 1.114367 False True False False False
692 1 2.268351 -0.427204 2.730158 0.930284 -0.896089 False True False False False
693 0 -1.607474 -0.427204 -0.814469 0.930284 -0.896089 False False False True False
694 0 1.642374 -0.427204 0.269425 -1.073405 -0.896089 True False False False False
695 1 0.142799 -0.427204 -0.829116 0.930284 -0.896089 True False False False False
696 0 -0.732338 -0.427204 0.181542 0.930284 -0.896089 True False False False False
697 0 -0.232479 -0.427204 -0.389700 0.930284 -0.896089 False True False False False
698 0 1.268634 -0.427204 -0.272522 0.930284 1.114367 False True False False False
699 0 0.267379 -0.427204 0.650253 -1.073405 -0.896089 False True False False False

将样本示例全集分割为训练样本和测试样本，测试样本占比为30%，设定随机数种子为123，以保证随机抽样的结果可重复。

# 设置特征变量，即除V1之外的全部变量
X = data.drop(['V1', 'V3_5'], axis=1)
X['intercept'] = [1]*X.shape[0]
y = data['V1']
print(data["V1"].value_counts())

V1
0    517
1    183
Name: count, dtype: int64

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=123)
X_train.head()
V2 V4 V5 V6 V7 V3_1 V3_2 V3_3 V3_4 intercept
404 -0.483177 -0.427204 1.880619 -1.073405 1.114367 True False False False 1
63 -0.357059 -0.427204 -1.078119 -1.073405 1.114367 False True False False 1
34 1.893073 -0.427204 0.284072 -1.073405 -0.896089 False False False True 1
33 0.642658 -0.427204 -1.136708 0.930284 1.114367 False False False True 1
583 1.142516 -0.427204 -1.136708 -1.073405 1.114367 True False False False 1 
# 1. 确定需要转换的布尔列
bool_cols = ['V3_1', 'V3_2', 'V3_3', 'V3_4']

# 2. 将这些列从布尔型转换为整数型 (True -> 1, False -> 0)
X_train[bool_cols] = X_train[bool_cols].astype(int)
X_test[bool_cols] = X_test[bool_cols].astype(int)

y_train.head()

404    1
63     0
34     0
33     0
583    0
Name: V1, dtype: int64

5. 使用statsmodels建立二元Logistic回归算法模型

5.1 使用statsmodels建立二元Logistic回归算法模型

statsmodels 是一个Python库，它提供了许多用于统计建模、统计测试以及数据探索的工具。该库专注于为用户提供经典的统计方法，包括线性回归、广义线性模型、时间序列分析等。

model = sm.Logit(y_train, X_train)
results = model.fit()
results.params

Optimization terminated successfully.
         Current function value: 0.478264
         Iterations 6





V2          -0.364992
V4           0.026051
V5           0.999300
V6          -0.187733
V7           0.059041
V3_1        -0.521205
V3_2        -0.832842
V3_3         0.103559
V3_4         0.097942
intercept   -0.644774
dtype: float64

results.summary()
Logit Regression Results
Dep. Variable: V1 No. Observations: 490
Model: Logit Df Residuals: 480
Method: MLE Df Model: 9
Date: Sat, 27 Sep 2025 Pseudo R-squ.: 0.1792
Time: 10:43:33 Log-Likelihood: -234.35
converged: True LL-Null: -285.50
Covariance Type: nonrobust LLR p-value: 5.394e-18
coef std err z P>|z| [0.025 0.975]
V2 -0.3650 0.116 -3.139 0.002 -0.593 -0.137
V4 0.0261 0.110 0.238 0.812 -0.189 0.241
V5 0.9993 0.122 8.167 0.000 0.759 1.239
V6 -0.1877 0.116 -1.616 0.106 -0.415 0.040
V7 0.0590 0.116 0.511 0.610 -0.168 0.286
V3_1 -0.5212 1.168 -0.446 0.656 -2.811 1.769
V3_2 -0.8328 1.161 -0.717 0.473 -3.109 1.443
V3_3 0.1036 1.236 0.084 0.933 -2.319 2.526
V3_4 0.0979 1.187 0.082 0.934 -2.229 2.425
intercept -0.6448 1.145 -0.563 0.573 -2.889 1.600 
# 以几率比（Odds ratio）的形式输出二元Logistic回归模型的系数值
np.exp(results.params)

V2           0.694203
V4           1.026393
V5           2.716381
V6           0.828836
V7           1.060819
V3_1         0.593805
V3_2         0.434812
V3_3         1.109111
V3_4         1.102899
intercept    0.524781
dtype: float64
变量 几率比 (OR) 几率变化 解释 (购买倾向)
V5 2.716 增加 171.6% 最强正向影响。V5 每增加 1 单位，客户购买的几率增加约 172%。
V2 0.694 减少 30.6% 显著负向影响。V2 每增加 1 单位，客户购买的几率减少约 31%。
V7 1.061 增加 6.1% 微弱的正向影响。
V4 1.026 增加 2.6% 几乎无影响。
V6 0.829 减少 17.1% 较弱的负向影响。 
margeff = results.get_margeff()
margeff.summary()
Logit Marginal Effects
Dep. Variable: V1
Method: dydx
At: overall
dy/dx std err z P>|z| [0.025 0.975]
V2 -0.0565 0.017 -3.235 0.001 -0.091 -0.022
V4 0.0040 0.017 0.238 0.812 -0.029 0.037
V5 0.1546 0.014 10.901 0.000 0.127 0.182
V6 -0.0290 0.018 -1.629 0.103 -0.064 0.006
V7 0.0091 0.018 0.511 0.609 -0.026 0.044
V3_1 -0.0806 0.181 -0.446 0.655 -0.435 0.274
V3_2 -0.1289 0.179 -0.718 0.473 -0.481 0.223
V3_3 0.0160 0.191 0.084 0.933 -0.359 0.391
V3_4 0.0152 0.184 0.083 0.934 -0.345 0.375

5.1 计算训练误差

table = results.pred_table() 
table

array([[334.,  24.],
       [ 86.,  46.]])

# 计算模型的准确率
Accuracy = (table[0, 0] + table[1, 1]) / np.sum(table)
Accuracy

0.7755102040816326

# 计算模型的错误率
Error_rate = 1 - Accuracy
Error_rate

0.22448979591836737

# 计算模型的精确率
precision = table[1, 1] / (table[0, 1] + table[1, 1])
precision

0.6571428571428571

# 计算模型的召回率
recall = table[1, 1] / (table[1, 0] + table[1, 1])
recall

0.3484848484848485

5.3 计算测试误差

prob = results.predict(X_test)
pred = (prob >= 0.5)
table = pd.crosstab(y_test, pred, colnames=['Predicted'])
table
Predicted False True
V1
0 144 15
1 34 17 
table = np.array(table)   

Accuracy = (table[0, 0] + table[1, 1]) / np.sum(table)
Accuracy

0.7666666666666667

Error_rate = 1 - Accuracy
Error_rate

0.23333333333333328

precision = table[1, 1] / (table[0, 1] + table[1, 1])
precision

0.53125

recall = table[1, 1] / (table[1, 0] + table[1, 1])
recall

0.3333333333333333

6 使用sklearn建立二元Logistic回归算法模型

model =  LogisticRegression(C=1e10, fit_intercept=True)
model.fit(X_train, y_train)
print("训练样本预测准确率: {:.3f}".format(model.score(X_train, y_train)))
print("测试样本预测准确率: {:.3f}".format(model.score(X_test, y_test)))

训练样本预测准确率: 0.778
测试样本预测准确率: 0.767

model.coef_

array([[-0.36499569,  0.02606894,  0.99933449, -0.18780027,  0.05900235,
        -0.52126282, -0.83295411,  0.10357227,  0.09778604, -0.32232961]])

predict_target = model.predict(X_test)
predict_target

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

predict_target_prob = model.predict_proba(X_test)  
predict_target_prob

array([[0.51763559, 0.48236441],
       [0.51629213, 0.48370787],
       [0.91085971, 0.08914029],
       [0.80692919, 0.19307081],
       [0.46592783, 0.53407217],
       [0.63140839, 0.36859161],
       [0.13433822, 0.86566178],
       [0.88889992, 0.11110008],
       [0.82062785, 0.17937215],
       [0.76965345, 0.23034655],
       [0.28261318, 0.71738682],
       [0.35834838, 0.64165162],
       [0.86334216, 0.13665784],
       [0.83607065, 0.16392935],
       [0.8698404 , 0.1301596 ],
       [0.49355146, 0.50644854],
       [0.91191593, 0.08808407],
       [0.62518094, 0.37481906],
       [0.77131462, 0.22868538],
       [0.8886198 , 0.1113802 ],
       [0.89276882, 0.10723118],
       [0.7414543 , 0.2585457 ],
       [0.8080439 , 0.1919561 ],
       [0.48794502, 0.51205498],
       [0.90765422, 0.09234578],
       [0.8010581 , 0.1989419 ],
       [0.82582916, 0.17417084],
       [0.679377  , 0.320623  ],
       [0.82005153, 0.17994847],
       [0.88156376, 0.11843624],
       [0.79245504, 0.20754496],
       [0.93622636, 0.06377364],
       [0.78685809, 0.21314191],
       [0.85812394, 0.14187606],
       [0.642984  , 0.357016  ],
       [0.64285092, 0.35714908],
       [0.90753656, 0.09246344],
       [0.64783214, 0.35216786],
       [0.85586175, 0.14413825],
       [0.3797633 , 0.6202367 ],
       [0.79330016, 0.20669984],
       [0.63976419, 0.36023581],
       [0.37634959, 0.62365041],
       [0.91948825, 0.08051175],
       [0.66908071, 0.33091929],
       [0.92954157, 0.07045843],
       [0.8075017 , 0.1924983 ],
       [0.43342784, 0.56657216],
       [0.90516397, 0.09483603],
       [0.92356806, 0.07643194],
       [0.80544403, 0.19455597],
       [0.48734119, 0.51265881],
       [0.85002795, 0.14997205],
       [0.79957494, 0.20042506],
       [0.76162742, 0.23837258],
       [0.73278302, 0.26721698],
       [0.8944985 , 0.1055015 ],
       [0.9133222 , 0.0866778 ],
       [0.85281039, 0.14718961],
       [0.89742801, 0.10257199],
       [0.95215385, 0.04784615],
       [0.93748723, 0.06251277],
       [0.93879853, 0.06120147],
       [0.63699059, 0.36300941],
       [0.78121681, 0.21878319],
       [0.88156762, 0.11843238],
       [0.65374122, 0.34625878],
       [0.57162573, 0.42837427],
       [0.93968873, 0.06031127],
       [0.90585719, 0.09414281],
       [0.37448456, 0.62551544],
       [0.93559087, 0.06440913],
       [0.91715311, 0.08284689],
       [0.84468944, 0.15531056],
       [0.76426803, 0.23573197],
       [0.93394464, 0.06605536],
       [0.87272681, 0.12727319],
       [0.83415758, 0.16584242],
       [0.04009461, 0.95990539],
       [0.91245489, 0.08754511],
       [0.67711562, 0.32288438],
       [0.86259866, 0.13740134],
       [0.73356674, 0.26643326],
       [0.61847045, 0.38152955],
       [0.91366999, 0.08633001],
       [0.63472297, 0.36527703],
       [0.10117662, 0.89882338],
       [0.80878858, 0.19121142],
       [0.80824599, 0.19175401],
       [0.92755529, 0.07244471],
       [0.83357155, 0.16642845],
       [0.76159487, 0.23840513],
       [0.91049177, 0.08950823],
       [0.606099  , 0.393901  ],
       [0.85053425, 0.14946575],
       [0.74102107, 0.25897893],
       [0.63458652, 0.36541348],
       [0.91207684, 0.08792316],
       [0.85542491, 0.14457509],
       [0.93039815, 0.06960185],
       [0.70341192, 0.29658808],
       [0.58131721, 0.41868279],
       [0.32371423, 0.67628577],
       [0.80355132, 0.19644868],
       [0.79605469, 0.20394531],
       [0.88424795, 0.11575205],
       [0.61076991, 0.38923009],
       [0.35058916, 0.64941084],
       [0.93603506, 0.06396494],
       [0.88874086, 0.11125914],
       [0.88324437, 0.11675563],
       [0.0515678 , 0.9484322 ],
       [0.8968454 , 0.1031546 ],
       [0.6683395 , 0.3316605 ],
       [0.35755575, 0.64244425],
       [0.79999772, 0.20000228],
       [0.47403203, 0.52596797],
       [0.88244952, 0.11755048],
       [0.50049718, 0.49950282],
       [0.73810985, 0.26189015],
       [0.59322973, 0.40677027],
       [0.96687806, 0.03312194],
       [0.83244493, 0.16755507],
       [0.63821928, 0.36178072],
       [0.94857156, 0.05142844],
       [0.9408889 , 0.0591111 ],
       [0.85223514, 0.14776486],
       [0.91832346, 0.08167654],
       [0.58065592, 0.41934408],
       [0.51597658, 0.48402342],
       [0.62239748, 0.37760252],
       [0.65539784, 0.34460216],
       [0.88421676, 0.11578324],
       [0.8138028 , 0.1861972 ],
       [0.8169248 , 0.1830752 ],
       [0.94012532, 0.05987468],
       [0.27994632, 0.72005368],
       [0.70296231, 0.29703769],
       [0.89036651, 0.10963349],
       [0.95551123, 0.04448877],
       [0.40620528, 0.59379472],
       [0.91089592, 0.08910408],
       [0.83359661, 0.16640339],
       [0.71691635, 0.28308365],
       [0.70373702, 0.29626298],
       [0.41249923, 0.58750077],
       [0.81253331, 0.18746669],
       [0.90378517, 0.09621483],
       [0.75770748, 0.24229252],
       [0.67206838, 0.32793162],
       [0.95326391, 0.04673609],
       [0.65161348, 0.34838652],
       [0.87662762, 0.12337238],
       [0.90589838, 0.09410162],
       [0.26063   , 0.73937   ],
       [0.64948499, 0.35051501],
       [0.9496182 , 0.0503818 ],
       [0.62115565, 0.37884435],
       [0.95081659, 0.04918341],
       [0.83680347, 0.16319653],
       [0.64476694, 0.35523306],
       [0.26685784, 0.73314216],
       [0.8867543 , 0.1132457 ],
       [0.62056352, 0.37943648],
       [0.88706933, 0.11293067],
       [0.41991738, 0.58008262],
       [0.93257236, 0.06742764],
       [0.66353464, 0.33646536],
       [0.9437626 , 0.0562374 ],
       [0.22202805, 0.77797195],
       [0.87305977, 0.12694023],
       [0.93721479, 0.06278521],
       [0.59524246, 0.40475754],
       [0.30004369, 0.69995631],
       [0.92634292, 0.07365708],
       [0.85470288, 0.14529712],
       [0.87197324, 0.12802676],
       [0.92257891, 0.07742109],
       [0.83035449, 0.16964551],
       [0.80864914, 0.19135086],
       [0.69348381, 0.30651619],
       [0.52604653, 0.47395347],
       [0.83915518, 0.16084482],
       [0.92618187, 0.07381813],
       [0.44448443, 0.55551557],
       [0.85883378, 0.14116622],
       [0.53950342, 0.46049658],
       [0.84912054, 0.15087946],
       [0.85258155, 0.14741845],
       [0.443867  , 0.556133  ],
       [0.89995135, 0.10004865],
       [0.42192742, 0.57807258],
       [0.94839723, 0.05160277],
       [0.77554517, 0.22445483],
       [0.81734555, 0.18265445],
       [0.91186786, 0.08813214],
       [0.83354801, 0.16645199],
       [0.417775  , 0.582225  ],
       [0.88765343, 0.11234657],
       [0.81629596, 0.18370404],
       [0.4718842 , 0.5281158 ],
       [0.37422986, 0.62577014],
       [0.60405928, 0.39594072],
       [0.87033048, 0.12966952],
       [0.75082848, 0.24917152],
       [0.83975351, 0.16024649],
       [0.88324818, 0.11675182],
       [0.89606241, 0.10393759],
       [0.84257166, 0.15742834],
       [0.90915068, 0.09084932]])

predict_target_prob_lr = predict_target_prob[:, 1]
df = pd.DataFrame({'prob': predict_target_prob_lr, 'target': predict_target, 'labels': list(y_test)})
df.head()
prob target labels
0 0.482364 0 0
1 0.483708 0 0
2 0.089140 0 0
3 0.193071 0 0
4 0.534072 1 0 
print('预测正确总数：')
print(sum(predict_target==y_test))

预测正确总数：
161

print('训练样本：')
predict_Target = model.predict(X_train)
print(metrics.classification_report(y_train, predict_Target))
print(metrics.confusion_matrix(y_train, predict_Target))

训练样本：
              precision    recall  f1-score   support

           0       0.80      0.93      0.86       358
           1       0.66      0.36      0.46       132

    accuracy                           0.78       490
   macro avg       0.73      0.64      0.66       490
weighted avg       0.76      0.78      0.75       490

[[334  24]
 [ 85  47]]

print('测试样本：')
print(metrics.classification_report(y_test, predict_target))
print(metrics.confusion_matrix(y_test, predict_target))

测试样本：
              precision    recall  f1-score   support

           0       0.81      0.91      0.85       159
           1       0.53      0.33      0.41        51

    accuracy                           0.77       210
   macro avg       0.67      0.62      0.63       210
weighted avg       0.74      0.77      0.75       210

[[144  15]
 [ 34  17]]

7 特征变量重要性水平分析

lr1 = [i for item in model.coef_ for i in item]
lr1 = np.array(lr1)
lr1

array([-0.36499569,  0.02606894,  0.99933449, -0.18780027,  0.05900235,
       -0.52126282, -0.83295411,  0.10357227,  0.09778604, -0.32232961])

feature = list(X.columns)
feature

['V2', 'V4', 'V5', 'V6', 'V7', 'V3_1', 'V3_2', 'V3_3', 'V3_4', 'intercept']

dic={}
for i in range(len(feature)):
  dic.update({feature[i]: lr1[i]})
dic

{'V2': -0.36499568695644263,
 'V4': 0.026068941454863492,
 'V5': 0.9993344932608373,
 'V6': -0.18780026510906955,
 'V7': 0.05900234918715404,
 'V3_1': -0.5212628192766355,
 'V3_2': -0.8329541134834083,
 'V3_3': 0.10357227463321901,
 'V3_4': 0.09778604005038755,
 'intercept': -0.32232961040511104}

df = df.reset_index().rename(columns={'index':'特征'})
df
特征 prob target labels
0 0 0.482364 0 0
1 1 0.483708 0 0
2 2 0.089140 0 0
3 3 0.193071 0 0
4 4 0.534072 1 0
5 5 0.368592 0 0
6 6 0.865662 1 0
7 7 0.111100 0 0
8 8 0.179372 0 0
9 9 0.230347 0 0
10 10 0.717387 1 1
11 11 0.641652 1 1
12 12 0.136658 0 0
13 13 0.163929 0 0
14 14 0.130160 0 0
15 15 0.506449 1 0
16 16 0.088084 0 0
17 17 0.374819 0 0
18 18 0.228685 0 0
19 19 0.111380 0 0
20 20 0.107231 0 0
21 21 0.258546 0 1
22 22 0.191956 0 0
23 23 0.512055 1 0
24 24 0.092346 0 1
25 25 0.198942 0 1
26 26 0.174171 0 0
27 27 0.320623 0 0
28 28 0.179948 0 0
29 29 0.118436 0 0
30 30 0.207545 0 0
31 31 0.063774 0 0
32 32 0.213142 0 0
33 33 0.141876 0 0
34 34 0.357016 0 0
35 35 0.357149 0 1
36 36 0.092463 0 0
37 37 0.352168 0 0
38 38 0.144138 0 0
39 39 0.620237 1 1
40 40 0.206700 0 0
41 41 0.360236 0 1
42 42 0.623650 1 0
43 43 0.080512 0 0
44 44 0.330919 0 0
45 45 0.070458 0 0
46 46 0.192498 0 0
47 47 0.566572 1 0
48 48 0.094836 0 0
49 49 0.076432 0 0
50 50 0.194556 0 0
51 51 0.512659 1 1
52 52 0.149972 0 1
53 53 0.200425 0 0
54 54 0.238373 0 0
55 55 0.267217 0 1
56 56 0.105502 0 0
57 57 0.086678 0 0
58 58 0.147190 0 0
59 59 0.102572 0 0
60 60 0.047846 0 1
61 61 0.062513 0 0
62 62 0.061201 0 0
63 63 0.363009 0 0
64 64 0.218783 0 0
65 65 0.118432 0 0
66 66 0.346259 0 0
67 67 0.428374 0 0
68 68 0.060311 0 0
69 69 0.094143 0 0
70 70 0.625515 1 0
71 71 0.064409 0 1
72 72 0.082847 0 0
73 73 0.155311 0 0
74 74 0.235732 0 0
75 75 0.066055 0 0
76 76 0.127273 0 0
77 77 0.165842 0 0
78 78 0.959905 1 1
79 79 0.087545 0 0
80 80 0.322884 0 1
81 81 0.137401 0 0
82 82 0.266433 0 0
83 83 0.381530 0 0
84 84 0.086330 0 0
85 85 0.365277 0 0
86 86 0.898823 1 1
87 87 0.191211 0 0
88 88 0.191754 0 0
89 89 0.072445 0 0
90 90 0.166428 0 0
91 91 0.238405 0 0
92 92 0.089508 0 0
93 93 0.393901 0 0
94 94 0.149466 0 0
95 95 0.258979 0 1
96 96 0.365413 0 0
97 97 0.087923 0 1
98 98 0.144575 0 0
99 99 0.069602 0 0
100 100 0.296588 0 0
101 101 0.418683 0 0
102 102 0.676286 1 0
103 103 0.196449 0 0
104 104 0.203945 0 0
105 105 0.115752 0 0
106 106 0.389230 0 0
107 107 0.649411 1 1
108 108 0.063965 0 0
109 109 0.111259 0 0
110 110 0.116756 0 0
111 111 0.948432 1 1
112 112 0.103155 0 1
113 113 0.331660 0 0
114 114 0.642444 1 0
115 115 0.200002 0 0
116 116 0.525968 1 1
117 117 0.117550 0 0
118 118 0.499503 0 1
119 119 0.261890 0 0
120 120 0.406770 0 1
121 121 0.033122 0 0
122 122 0.167555 0 0
123 123 0.361781 0 0
124 124 0.051428 0 0
125 125 0.059111 0 0
126 126 0.147765 0 0
127 127 0.081677 0 0
128 128 0.419344 0 1
129 129 0.484023 0 1
130 130 0.377603 0 1
131 131 0.344602 0 0
132 132 0.115783 0 0
133 133 0.186197 0 1
134 134 0.183075 0 0
135 135 0.059875 0 1
136 136 0.720054 1 1
137 137 0.297038 0 0
138 138 0.109633 0 0
139 139 0.044489 0 0
140 140 0.593795 1 1
141 141 0.089104 0 0
142 142 0.166403 0 0
143 143 0.283084 0 0
144 144 0.296263 0 0
145 145 0.587501 1 1
146 146 0.187467 0 1
147 147 0.096215 0 0
148 148 0.242293 0 0
149 149 0.327932 0 1
150 150 0.046736 0 0
151 151 0.348387 0 1
152 152 0.123372 0 0
153 153 0.094102 0 0
154 154 0.739370 1 1
155 155 0.350515 0 0
156 156 0.050382 0 0
157 157 0.378844 0 1
158 158 0.049183 0 0
159 159 0.163197 0 0
160 160 0.355233 0 0
161 161 0.733142 1 1
162 162 0.113246 0 0
163 163 0.379436 0 1
164 164 0.112931 0 0
165 165 0.580083 1 0
166 166 0.067428 0 0
167 167 0.336465 0 1
168 168 0.056237 0 0
169 169 0.777972 1 0
170 170 0.126940 0 0
171 171 0.062785 0 0
172 172 0.404758 0 0
173 173 0.699956 1 0
174 174 0.073657 0 0
175 175 0.145297 0 1
176 176 0.128027 0 0
177 177 0.077421 0 0
178 178 0.169646 0 0
179 179 0.191351 0 0
180 180 0.306516 0 1
181 181 0.473953 0 0
182 182 0.160845 0 0
183 183 0.073818 0 0
184 184 0.555516 1 0
185 185 0.141166 0 0
186 186 0.460497 0 1
187 187 0.150879 0 0
188 188 0.147418 0 0
189 189 0.556133 1 1
190 190 0.100049 0 0
191 191 0.578073 1 1
192 192 0.051603 0 0
193 193 0.224455 0 0
194 194 0.182654 0 0
195 195 0.088132 0 0
196 196 0.166452 0 1
197 197 0.582225 1 1
198 198 0.112347 0 0
199 199 0.183704 0 1
200 200 0.528116 1 0
201 201 0.625770 1 0
202 202 0.395941 0 1
203 203 0.129670 0 1
204 204 0.249172 0 0
205 205 0.160246 0 1
206 206 0.116752 0 0
207 207 0.103938 0 0
208 208 0.157428 0 0
209 209 0.090849 0 0 
df = pd.DataFrame.from_dict(dic, orient='index', columns=['权重'])
df
权重
V2 -0.364996
V4 0.026069
V5 0.999334
V6 -0.187800
V7 0.059002
V3_1 -0.521263
V3_2 -0.832954
V3_3 0.103572
V3_4 0.097786
intercept -0.322330 
df = df.reset_index().rename(columns={'index':'特征'})
df
特征 权重
0 V2 -0.364996
1 V4 0.026069
2 V5 0.999334
3 V6 -0.187800
4 V7 0.059002
5 V3_1 -0.521263
6 V3_2 -0.832954
7 V3_3 0.103572
8 V3_4 0.097786
9 intercept -0.322330 
# 按照权重排序
df = df.sort_values(by='权重',ascending=False)
df
特征 权重
2 V5 0.999334
7 V3_3 0.103572
8 V3_4 0.097786
4 V7 0.059002
1 V4 0.026069
3 V6 -0.187800
9 intercept -0.322330
0 V2 -0.364996
5 V3_1 -0.521263
6 V3_2 -0.832954 
data_hight = df['权重'].values.tolist()
data_hight

[0.9993344932608373,
 0.10357227463321901,
 0.09778604005038755,
 0.05900234918715404,
 0.026068941454863492,
 -0.18780026510906955,
 -0.32232961040511104,
 -0.36499568695644263,
 -0.5212628192766355,
 -0.8329541134834083]

data_x = df['特征'].values.tolist()
data_x

['V5', 'V3_3', 'V3_4', 'V7', 'V4', 'V6', 'intercept', 'V2', 'V3_1', 'V3_2']

font = {'size': 7}
sns.set(font_scale=1.2)
plt.rc('font')
plt.figure(figsize=(6,6))
plt.barh(range(len(data_x)), data_hight, color='#6699CC')
plt.yticks(range(len(data_x)),data_x,fontsize=12)
plt.tick_params(labelsize=12)
plt.xlabel('Feature importance',fontsize=14)
plt.title("LR feature importance analysis",fontsize = 14)
plt.show()

png

8 绘制ROC曲线，计算AUC值

RocCurveDisplay.from_estimator(model, X_test, y_test)
x = np.linspace(0, 1, 100)
plt.plot(x, x, 'k--', linewidth=1)
plt.title('ROC Curve (Test Set)')

Text(0.5, 1.0, 'ROC Curve (Test Set)')

png

9 计算科恩kappa得分

科恩kappa得分（Cohen’s kappa score）是一种用于衡量两个评分者之间一致性的统计指标。它考虑了由于随机因素导致的一致性，比简单的准确率更能反映评分者之间的真实一致性。该指标的值范围在 -1 到 1 之间：

cohen_kappa_score(y_test, pred)

0.27361287590004235