2023-08-18,

大家好，我是Peter~

本文是一个基于kaggle机器学习实战案例：基于机器学习的信贷违约预测实战，采用了多种模型，最终结果随机森林模型排名第一。

主要内容包含：

数据基本信息与EDA 数据预处理与特征工程 多种模型预测及指标对比导入库 导入库

In [1]:

import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import missingno as mso import seaborn as sns import warnings import os import scipy from scipy import stats from scipy.stats import pearsonr from scipy.stats import ttest_ind from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from imblearn.over_sampling import SMOTE from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.naive_bayes import CategoricalNB from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV, RandomizedSearchCV from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") 数据基本信息 导入数据

In [2]:

df = pd.read_csv("data.csv") df.head()

Out[2]:

基本信息

In [3]:

# 整体的数据量 df.shape

Out[3]:

(614, 13)

In [4]:

# 全部字段 df.columns

Out[4]:

Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'], dtype='object')

可以看到主要是贷款人ID、性别、是否结婚、工作部门、受教育程度等字段信息

In [5]:

# 查看字段类型 df.dtypes

Out[5]:

Loan_ID object Gender object Married object Dependents object Education object Self_Employed object ApplicantIncome int64 CoapplicantIncome float64 LoanAmount float64 Loan_Amount_Term float64 Credit_History float64 Property_Area object Loan_Status object dtype: object

In [6]:

# 查看描述统计信息 df.describe()

Out[6]:

ApplicantIncome

CoapplicantIncome

LoanAmount

Loan_Amount_Term

Credit_History

count

614.000000

614.000000

592.000000

600.00000

564.000000

mean

5403.459283

1621.245798

146.412162

342.00000

0.842199

std

6109.041673

2926.248369

85.587325

65.12041

0.364878

min

150.000000

0.000000

9.000000

12.00000

0.000000

25%

2877.500000

0.000000

100.000000

360.00000

1.000000

50%

3812.500000

1188.500000

128.000000

360.00000

1.000000

75%

5795.000000

2297.250000

168.000000

360.00000

1.000000

max

81000.000000

41667.000000

700.000000

480.00000

1.000000

缺失值情况

In [7]:

df.isnull().sum()

Out[7]:

Loan_ID 0 Gender 13 Married 3 Dependents 15 Education 0 Self_Employed 32 ApplicantIncome 0 CoapplicantIncome 0 LoanAmount 22 Loan_Amount_Term 14 Credit_History 50 Property_Area 0 Loan_Status 0 dtype: int64

可以看到部分字段存在缺失值

In [8]:

mso.bar(df,color="blue") plt.show()

图片

后面会针对缺失值进行填充处理。数据探索EDA

数据探索EDA 分类型变量 Loan_ID

In [9]:

df.Loan_ID.value_counts(dropna=False)

Out[9]:

LP001002 1 LP002328 1 LP002305 1 LP002308 1 LP002314 1 .. LP001692 1 LP001693 1 LP001698 1 LP001699 1 LP002990 1 Name: Loan_ID, Length: 614, dtype: int64

可以看到每个Loan_ID刚好一条记录

Gender

In [10]:

df.Gender.value_counts(dropna=False)

Out[10]:

Male 489 Female 112 NaN 13 Name: Gender, dtype: int64

In [11]:

sns.countplot(x="Gender", data=df, palette="hls") plt.show()

图片

针对不同性别做缺失值处理：

In [12]:

countMale = len(df[df.Gender == 'Male']) # 男性数据 countFemale = len(df[df.Gender == 'Female']) # 女性数据 countNull = len(df[df.Gender.isnull()]) # 缺失值数量

In [13]:

print("Percentage of Male: {:.2f}%".format((countMale / (len(df.Gender)) * 100))) print("Percentage of Female: {:.2f}%".format((countFemale / (len(df.Gender)) * 100))) print("Missing values percentage: {:.2f}%".format((countNull / (len(df.Gender)) * 100))) Percentage of Male: 79.64% Percentage of Female: 18.24% Missing values percentage: 2.12% Married

In [14]:

df.Married.value_counts(dropna=False)

Out[14]:

Yes 398 No 213 NaN 3 Name: Married, dtype: int64

In [15]:

sns.countplot(x="Married", data=df, palette="Paired") plt.show()

图片

是否结婚的人群对比：

In [16]:

countMarried = len(df[df.Married == 'Yes']) countNotMarried = len(df[df.Married == 'No']) countNull = len(df[df.Married.isnull()])

In [17]:

print("Percentage of married: {:.2f}%".format((countMarried / (len(df.Married))*100))) print("Percentage of Not married applicant: {:.2f}%".format((countNotMarried / (len(df.Married))*100))) print("Missing values percentage: {:.2f}%".format((countNull / (len(df.Married))*100))) Percentage of married: 64.82% Percentage of Not married applicant: 34.69% Missing values percentage: 0.49% Education

In [18]:

df.Education.value_counts(dropna=False)

Out[18]:

Graduate 480 Not Graduate 134 Name: Education, dtype: int64

In [19]:

sns.countplot(x="Education", data=df, palette="rocket") plt.show()

图片

不同受教育程度的人群对比：

In [20]:

countGraduate = len(df[df.Education == 'Graduate']) countNotGraduate = len(df[df.Education == 'Not Graduate']) countNull = len(df[df.Education.isnull()]) print("Percentage of graduate applicant: {:.2f}%".format((countGraduate / (len(df.Education))*100))) print("Percentage of Not graduate applicant: {:.2f}%".format((countNotGraduate / (len(df.Education))*100))) print("Missing percentage: {:.2f}%".format((countNull / (len(df.Education))*100))) Percentage of graduate applicant: 78.18% Percentage of Not graduate applicant: 21.82% Missing percentage: 0.00% Self Employed

In [21]:

df.Self_Employed.value_counts(dropna=False)

Out[21]:

No 500 Yes 82 NaN 32 Name: Self_Employed, dtype: int64

In [22]:

sns.countplot(x="Self_Employed", data=df, palette="crest") plt.show()

图片

是否为自聘员工对比：

In [23]:

countNo = len(df[df.Self_Employed == 'No']) countYes = len(df[df.Self_Employed == 'Yes']) countNull = len(df[df.Self_Employed.isnull()]) print("Percentage of Not self employed: {:.2f}%".format((countNo / (len(df.Self_Employed))*100))) print("Percentage of self employed: {:.2f}%".format((countYes / (len(df.Self_Employed))*100))) print("Missing values percentage: {:.2f}%".format((countNull / (len(df.Self_Employed))*100))) Percentage of Not self employed: 81.43% Percentage of self employed: 13.36% Missing values percentage: 5.21% Credit History

In [24]:

df.Credit_History.value_counts(dropna=False)

Out[24]:

1.0 475 0.0 89 NaN 50 Name: Credit_History, dtype: int64

In [25]:

sns.countplot(x="Credit_History", data=df, palette="viridis") plt.show()

图片

是否有信用卡历史的人群对比：

In [26]:

count1 = len(df[df.Credit_History == 1]) count0 = len(df[df.Credit_History == 0]) countNull = len(df[df.Credit_History.isnull()])

In [27]:

print("Percentage of Good credit history: {:.2f}%".format((count1 / (len(df.Credit_History))*100))) print("Percentage of Bad credit history: {:.2f}%".format((count0 / (len(df.Credit_History))*100))) print("Missing values percentage: {:.2f}%".format((countNull / (len(df.Credit_History))*100))) Percentage of Good credit history: 77.36% Percentage of Bad credit history: 14.50% Missing values percentage: 8.14% Property Area

In [28]:

df.Property_Area.value_counts(dropna=False)

Out[28]:

Semiurban 233 Urban 202 Rural 179 Name: Property_Area, dtype: int64

In [29]:

sns.countplot(x="Property_Area", data=df, palette="cubehelix") plt.show()

图片

不同地区的人群对比：

In [30]:

countUrban = len(df[df.Property_Area == 'Urban']) countRural = len(df[df.Property_Area == 'Rural']) countSemiurban = len(df[df.Property_Area == 'Semiurban']) countNull = len(df[df.Property_Area.isnull()])

In [31]:

print("Percentage of Urban: {:.2f}%".format((countUrban / (len(df.Property_Area))*100))) print("Percentage of Rural: {:.2f}%".format((countRural / (len(df.Property_Area))*100))) print("Percentage of Semiurban: {:.2f}%".format((countSemiurban / (len(df.Property_Area))*100))) print("Missing values percentage: {:.2f}%".format((countNull / (len(df.Property_Area))*100))) Percentage of Urban: 32.90% Percentage of Rural: 29.15% Percentage of Semiurban: 37.95% Missing values percentage: 0.00%

这个字段在3个不同的取值下分布是均匀的，而且没有缺失值

Loan Status

In [32]:

df.Loan_Status.value_counts(dropna=False)

Out[32]:

Y 422 N 192 Name: Loan_Status, dtype: int64

In [33]:

sns.countplot(x="Loan_Status", data=df, palette="YlOrBr") plt.show()

图片

是否贷款的人群占比对比：

In [34]:

countY = len(df[df.Loan_Status == 'Y']) countN = len(df[df.Loan_Status == 'N']) countNull = len(df[df.Loan_Status.isnull()]) print("Percentage of Approved: {:.2f}%".format((countY / (len(df.Loan_Status))*100))) print("Percentage of Rejected: {:.2f}%".format((countN / (len(df.Loan_Status))*100))) print("Missing values percentage: {:.2f}%".format((countNull / (len(df.Loan_Status))*100))) Percentage of Approved: 68.73% Percentage of Rejected: 31.27% Missing values percentage: 0.00% Loan Amount Term

In [35]:

df.Loan_Amount_Term.value_counts(dropna=False)

Out[35]:

360.0 512 180.0 44 480.0 15 NaN 14 300.0 13 240.0 4 84.0 4 120.0 3 60.0 2 36.0 2 12.0 1 Name: Loan_Amount_Term, dtype: int64

In [36]:

sns.countplot(x="Loan_Amount_Term", data=df, palette="rocket") plt.show()

图片

不同贷款周期的人群对比：

In [37]:

count12 = len(df[df.Loan_Amount_Term == 12.0]) count36 = len(df[df.Loan_Amount_Term == 36.0]) count60 = len(df[df.Loan_Amount_Term == 60.0]) count84 = len(df[df.Loan_Amount_Term == 84.0]) count120 = len(df[df.Loan_Amount_Term == 120.0]) count180 = len(df[df.Loan_Amount_Term == 180.0]) count240 = len(df[df.Loan_Amount_Term == 240.0]) count300 = len(df[df.Loan_Amount_Term == 300.0]) count360 = len(df[df.Loan_Amount_Term == 360.0]) count480 = len(df[df.Loan_Amount_Term == 480.0]) countNull = len(df[df.Loan_Amount_Term.isnull()]) print("Percentage of 12: {:.2f}%".format((count12 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 36: {:.2f}%".format((count36 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 60: {:.2f}%".format((count60 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 84: {:.2f}%".format((count84 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 120: {:.2f}%".format((count120 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 180: {:.2f}%".format((count180 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 240: {:.2f}%".format((count240 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 300: {:.2f}%".format((count300 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 360: {:.2f}%".format((count360 / (len(df.Loan_Amount_Term))*100))) print("Percentage of 480: {:.2f}%".format((count480 / (len(df.Loan_Amount_Term))*100))) print("Missing values percentage: {:.2f}%".format((countNull / (len(df.Loan_Amount_Term))*100))) Percentage of 12: 0.16% Percentage of 36: 0.33% Percentage of 60: 0.33% Percentage of 84: 0.65% Percentage of 120: 0.49% Percentage of 180: 7.17% Percentage of 240: 0.65% Percentage of 300: 2.12% Percentage of 360: 83.39% Percentage of 480: 2.44% Missing values percentage: 2.28% 数值型变量 描述统计信息

In [38]:

df.select_dtypes(exclude=["object"]).columns

Out[38]:

Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History'], dtype='object')

In [39]:

df[['ApplicantIncome','CoapplicantIncome','LoanAmount']].describe()

Out[39]:

ApplicantIncome

CoapplicantIncome

LoanAmount

count

614.000000

614.000000

592.000000

mean

5403.459283

1621.245798

146.412162

std

6109.041673

2926.248369

85.587325

min

150.000000

0.000000

9.000000

25%

2877.500000

0.000000

100.000000

50%

3812.500000

1188.500000

128.000000

75%

5795.000000

2297.250000

168.000000

max

81000.000000

41667.000000

700.000000

字段直方图分布

In [40]:

sns.set(style="darkgrid") fig, axs = plt.subplots(2, 2, figsize=(10, 8)) sns.histplot(data=df, x="ApplicantIncome", kde=True, ax=axs[0, 0], color='green') sns.histplot(data=df, x="CoapplicantIncome", kde=True, ax=axs[0, 1], color='skyblue') sns.histplot(data=df, x="LoanAmount", kde=True, ax=axs[1, 0], color='orange');

图片

可以看到这3个字段呈现一定的偏态，后面会做数据转换处理。

字段小提琴图分布

In [41]:

sns.set(style="darkgrid") fig, axs1 = plt.subplots(2, 2, figsize=(10, 10)) sns.violinplot(data=df, y="ApplicantIncome", ax=axs1[0, 0], color='green') sns.violinplot(data=df, y="CoapplicantIncome", ax=axs1[0, 1], color='skyblue') sns.violinplot(data=df, y="LoanAmount", ax=axs1[1, 0], color='orange');

图片

两两特征分布 两个分类型变量

分类型变量主要是基于统计分析查看分布情况：

In [42]:

pd.crosstab(df.Gender,df.Married).plot(kind="bar", stacked=True, figsize=(5,5), color=['#f64f59','#12c2e9']) plt.title('Gender vs Married') plt.xlabel('Gender') plt.ylabel('Frequency') plt.xticks(rotatinotallow=0) plt.show()

图片

好坏信用卡历史对比：

In [43]:

pd.crosstab(df.Self_Employed,df.Credit_History).plot(kind="bar", stacked=True, figsize=(5,5), color=['#544a7d','#ffd452']) plt.title('Self Employed vs Credit History') plt.xlabel('Self Employed') plt.ylabel('Frequency') plt.legend(["Bad Credit", "Good Credit"]) plt.xticks(rotatinotallow=0) plt.show()

图片

不同地区的人群是否贷款对比：

In [44]:

pd.crosstab(df.Property_Area,df.Loan_Status).plot(kind="bar", stacked=True, figsize=(5,5), color=['#333333','#dd1818']) plt.title('Property Area vs Loan Status') plt.xlabel('Property Area') plt.ylabel('Frequency') plt.xticks(rotatinotallow=0) plt.show()

图片

分类型+数值型变量

In [45]:

sns.boxplot(x="Loan_Status", y="ApplicantIncome", data=df, palette="mako");

图片

sns.boxplot(x="CoapplicantIncome", y="Loan_Status", data=df, palette="rocket");

图片

sns.boxplot(x="Loan_Status", y="LoanAmount", data=df, palette="YlOrBr");

图片

两个数值型变量

In [48]:

df.plot(x='ApplicantIncome', y='CoapplicantIncome', style='o') plt.title('Applicant Income - Co Applicant Income') plt.xlabel('ApplicantIncome') plt.ylabel('CoapplicantIncome') plt.show()

图片

相关性计算：

In [49]:

print('Pearson correlation:', df['ApplicantIncome'].corr(df['CoapplicantIncome'])) print('T Test and P value: \n', stats.ttest_ind(df['ApplicantIncome'], df['CoapplicantIncome'])) Pearson correlation: -0.11660458122889966 T Test and P value: Ttest_indResult(statistic=13.835753259915661, pvalue=1.4609839484240346e-40) 相关性分析

In [50]:

plt.figure(figsize=(10,7)) sns.heatmap(df.corr(), annot=True, cmap='inferno') plt.show()

图片

特征工程（数据预处理） 删除无效变量Drop Unecessary Variables

In [51]:

df.drop("Loan_ID",axis=1, inplace=True) 填充缺失值Data Imputation

In [52]:

df.isnull().sum()

Out[52]:

Gender 13 Married 3 Dependents 15 Education 0 Self_Employed 32 ApplicantIncome 0 CoapplicantIncome 0 LoanAmount 22 Loan_Amount_Term 14 Credit_History 50 Property_Area 0 Loan_Status 0 dtype: int64

In [53]:

df.dtypes

Out[53]:

Gender object Married object Dependents object Education object Self_Employed object ApplicantIncome int64 CoapplicantIncome float64 LoanAmount float64 Loan_Amount_Term float64 Credit_History float64 Property_Area object Loan_Status object dtype: object

In [54]:

df["Credit_History"].value_counts()

Out[54]:

1.0 475 0.0 89 Name: Credit_History, dtype: int64

In [55]:

df["Loan_Amount_Term"].value_counts()

Out[55]:

360.0 512 180.0 44 480.0 15 300.0 13 240.0 4 84.0 4 120.0 3 60.0 2 36.0 2 12.0 1 Name: Loan_Amount_Term, dtype: int64 分类型变量

针对分类型变量的缺失值，我们使用众数mode进行填充：

In [56]:

df['Gender'].fillna(df['Gender'].mode()[0],inplace=True) df['Married'].fillna(df['Married'].mode()[0],inplace=True) df['Dependents'].fillna(df['Dependents'].mode()[0],inplace=True) df['Self_Employed'].fillna(df['Self_Employed'].mode()[0],inplace=True) # 信用卡历史记录 0-bad credit 1-good credit history df['Credit_History'].fillna(df['Credit_History'].mode()[0],inplace=True) # 还款周期（天） df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0],inplace=True) 数值型变量

数值型变量的缺失值使用均值mean进行填充

In [57]:

df['LoanAmount'].fillna(df['LoanAmount'].mean(),inplace=True) # 贷款金额 独热码One-hot Encoding

In [58]:

df = pd.get_dummies(df) df.head()

处理后的全部字段信息：

In [59]:

df.columns

Out[59]:

Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History', 'Gender_Female', 'Gender_Male', 'Married_No', 'Married_Yes', 'Dependents_0', 'Dependents_1', 'Dependents_2', 'Dependents_3+', 'Education_Graduate', 'Education_Not Graduate', 'Self_Employed_No', 'Self_Employed_Yes', 'Property_Area_Rural', 'Property_Area_Semiurban', 'Property_Area_Urban', 'Loan_Status_N', 'Loan_Status_Y'], dtype='object')

In [60]:

# 删除部分字段 df = df.drop(['Gender_Female', 'Married_No', 'Education_Not Graduate','Self_Employed_No', 'Loan_Status_N'], axis = 1)

In [61]:

# 字段重命名 new = {'Gender_Male': 'Gender', 'Married_Yes': 'Married', 'Education_Graduate': 'Education', 'Self_Employed_Yes': 'Self_Employed', 'Loan_Status_Y': 'Loan_Status'} df.rename(columns=new, inplace=True) 删除离群点remove outliers

以上下4分位数作为临界点：

In [62]:

Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)] 偏态分布处理Skewed Distribution Treatment

对数据开平方做数据转换：np.sqrt

In [63]:

df.ApplicantIncome = np.sqrt(df.ApplicantIncome) df.CoapplicantIncome = np.sqrt(df.CoapplicantIncome) df.LoanAmount = np.sqrt(df.LoanAmount)

再次查看数据分布：

In [64]:

sns.set(style="darkgrid") fig, axs = plt.subplots(2, 2, figsize=(10, 8)) sns.histplot(data=df, x="ApplicantIncome", kde=True, ax=axs[0, 0], color='green') sns.histplot(data=df, x="CoapplicantIncome", kde=True, ax=axs[0, 1], color='skyblue') sns.histplot(data=df, x="LoanAmount", kde=True, ax=axs[1, 0], color='orange');

图片

建模 特征分离

In [65]:

X = df.drop(["Loan_Status"], axis=1) y = df["Loan_Status"] SMOTE上采样

In [66]:

pd.value_counts(y) # 采样前

Out[66]:

1 112 0 24 Name: Loan_Status, dtype: int64

In [67]:

X, y = SMOTE().fit_resample(X, y)

In [68]:

pd.value_counts(y) # 采样后

Out[68]:

1 112 0 112 Name: Loan_Status, dtype: int64

In [69]:

sns.set_theme(style="darkgrid") sns.countplot(y=y, data=df, palette="coolwarm") plt.ylabel('Loan Status') plt.xlabel('Total') plt.show()

图片

数据标准化Data Normalization

In [70]:

mm = MinMaxScaler() X = mm.fit_transform(X) 切分训练集和测试集

In [71]:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) 模型1-Logistic Regression

In [72]:

pd.value_counts(y_train)

Out[72]:

1 90 0 89 Name: Loan_Status, dtype: int64

In [73]:

pd.value_counts(y_test)

Out[73]:

0 23 1 22 Name: Loan_Status, dtype: int64

In [74]:

LRclassifier = LogisticRegression(solver='saga', max_iter=500, random_state=1) LRclassifier.fit(X_train, y_train) # 模型预测 y_pred = LRclassifier.predict(X_test)

In [75]:

print(classification_report(y_test, y_pred)) # 分类结果报告 precision recall f1-score support 0 0.86 0.83 0.84 23 1 0.83 0.86 0.84 22 accuracy 0.84 45 macro avg 0.84 0.84 0.84 45 weighted avg 0.85 0.84 0.84 45

In [76]:

print(confusion_matrix(y_test, y_pred)) # 混淆矩阵 [[19 4] [ 3 19]]

In [77]:

LRAcc = accuracy_score(y_pred,y_test) # 准确率 print('LR accuracy: {:.2f}%'.format(LRAcc * 100)) LR accuracy: 84.44% 模型2-K-Nearest Neighbour(KNN)

新版本报错解决，参考：https://blog.csdn.net/weixin_51723388/article/details/128577782

在使用KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)时，只有在weights='uniform' 时才会用到stats.mode。其中uniform是均等权重，即邻域中的所有点的权重相等，相当于取众数。可将其改为weights='distance'

In [78]:

score_list = [] for i in range(1,21): knn = KNeighborsClassifier(n_neighbors = i,weights='distance') knn.fit(X_train, y_train) score_list.append(knn.score(X_test, y_test)) # 测试集预测得分 plt.plot(range(1,21), score_list) plt.xticks(np.arange(1,21,1)) plt.xlabel("K value") plt.ylabel("Score") plt.show() KNAcc = max(score_list) print("KNN best accuracy: {:.2f}%".format(KNAcc*100)) KNN best accuracy: 86.67%

图片

模型3-支持向量机Support Vector Machine (SVM)

In [79]:

svc = SVC(kernel='rbf', max_iter=500) svc.fit(X_train, y_train) y_pred = svc.predict(X_test) print(classification_report(y_test, y_pred)) print(confusion_matrix(y_test, y_pred)) precision recall f1-score support 0 0.95 0.78 0.86 23 1 0.81 0.95 0.88 22 accuracy 0.87 45 macro avg 0.88 0.87 0.87 45 weighted avg 0.88 0.87 0.87 45 [[18 5] [ 1 21]]

In [80]:

SVCAcc = accuracy_score(y_pred,y_test) print('SVC accuracy: {:.2f}%'.format(SVCAcc*100)) SVC accuracy: 86.67% 模型4-高斯朴素贝叶斯Gaussian NB

In [81]:

NBclassifier2 = GaussianNB() NBclassifier2.fit(X_train, y_train) y_pred = NBclassifier2.predict(X_test) print(classification_report(y_test, y_pred)) print(confusion_matrix(y_test, y_pred)) precision recall f1-score support 0 0.68 0.83 0.75 23 1 0.76 0.59 0.67 22 accuracy 0.71 45 macro avg 0.72 0.71 0.71 45 weighted avg 0.72 0.71 0.71 45 [[19 4] [ 9 13]]

In [82]:

GNBAcc = accuracy_score(y_pred,y_test) print('Gaussian Naive Bayes accuracy: {:.2f}%'.format(GNBAcc*100)) Gaussian Naive Bayes accuracy: 71.11% 模型5-决策树Decision Tree

In [83]:

scoreListDT = [] for i in range(2,21): DTclassifier = DecisionTreeClassifier(max_leaf_nodes=i) DTclassifier.fit(X_train, y_train) scoreListDT.append(DTclassifier.score(X_test, y_test)) plt.plot(range(2,21), scoreListDT) plt.xticks(np.arange(2,21,1)) plt.xlabel("Leaf") plt.ylabel("Score") plt.show() DTAcc = max(scoreListDT) print("Decision Tree Accuracy: {:.2f}%".format(DTAcc*100)) Decision Tree Accuracy: 84.44%

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模型6-随机森林Random Forest

In [84]:

scoreListRF = [] for i in range(2,25): RFclassifier = RandomForestClassifier(n_estimators = 1000, random_state = 1, max_leaf_nodes=i) RFclassifier.fit(X_train, y_train) scoreListRF.append(RFclassifier.score(X_test, y_test)) plt.plot(range(2,25), scoreListRF) plt.xticks(np.arange(2,25,1)) plt.xlabel("RF Value") plt.ylabel("Score") plt.show() RFAcc = max(scoreListRF) print("Random Forest Accuracy: {:.2f}%".format(RFAcc*100)) Random Forest Accuracy: 91.11%

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模型7-梯度提升树Gradient Boosting

In [85]:

# 设置参数 params={'n_estimators':[100,200,300,400,500], 'max_depth':[1,2,3,4,5], 'subsample':[0.5,1], 'max_leaf_nodes':[2,5,10,20,30,40,50]}

In [86]:

# 基于随机搜索查找参数组合 GB = RandomizedSearchCV(GradientBoostingClassifier(), params, cv=20) GB.fit(X_train, y_train)

Out[86]:

RandomizedSearchCV(cv=20, estimator=GradientBoostingClassifier(), param_distributinotallow={'max_depth': [1, 2, 3, 4, 5], 'max_leaf_nodes': [2, 5, 10, 20, 30, 40, 50], 'n_estimators': [100, 200, 300, 400, 500], 'subsample': [0.5, 1]})

In [87]:

print(GB.best_estimator_) print(GB.best_score_) GradientBoostingClassifier(max_depth=4, max_leaf_nodes=10, n_estimators=500, subsample=1) 0.7993055555555555

In [88]:

print(GB.best_params_) # 最佳参数组合 {'subsample': 1, 'n_estimators': 500, 'max_leaf_nodes': 10, 'max_depth': 4}

In [89]:

GB.best_params_["subsample"]

Out[89]:

1

基于查找到的参数再重新建模：

In [90]:

gbc = GradientBoostingClassifier(subsample=GB.best_params_["subsample"], n_estimators=GB.best_params_["n_estimators"], max_depth=GB.best_params_["max_depth"], max_leaf_nodes=GB.best_params_["max_leaf_nodes"], ) gbc.fit(X_train, y_train) y_pred = gbc.predict(X_test) print(classification_report(y_test, y_pred)) print(confusion_matrix(y_test, y_pred)) precision recall f1-score support 0 0.78 0.91 0.84 23 1 0.89 0.73 0.80 22 accuracy 0.82 45 macro avg 0.83 0.82 0.82 45 weighted avg 0.83 0.82 0.82 45 [[21 2] [ 6 16]]

In [91]:

GBAcc = accuracy_score(y_pred,y_test) print('Gradient Boosting accuracy: {:.2f}%'.format(GBAcc*100)) Gradient Boosting accuracy: 82.22% 模型比较

In [92]:

models = pd.DataFrame({'Model': ['Logistic Regression', 'K Neighbors', 'Support Vector Machine', 'Gaussian NB', 'Decision Tree', 'Random Forest', 'Gradient Boost'], 'Accuracy': [LRAcc*100, KNAcc*100, SVCAcc*100,GNBAcc*100, DTAcc*100, RFAcc*100, GBAcc*100]}) models.sort_values(by='Accuracy', ascending=False)

Out[92]:

Model

Accuracy

5

Random Forest

91.111111

1

K Neighbors

86.666667

2

Support Vector Machine

86.666667

0

Logistic Regression

84.444444

4

Decision Tree

84.444444

6

Gradient Boost

82.222222

3

Gaussian NB

71.111111

PS:本文来源：信贷违约预测建模，随机森林91.1%登顶！,信贷违约,机器学习,人工智能,作者：尤而小屋