一周算法项目实践（三）

最新推荐文章于 2025-06-26 09:04:05 发布

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最新推荐文章于 2025-06-26 09:04:05 发布

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分类专栏：一周算法实践文章标签： python

本文链接：https://blog.youkuaiyun.com/weixin_43726527/article/details/85173363

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本文实践了7种分类模型（逻辑回归、SVM、决策树等）对金融贷款逾期的预测，详细记录了accuracy、precision、recall、F1-score和AUC值，并绘制了ROC曲线，展示了模型评分汇总。

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金融贷款逾期的模型评分

1.数据信息：金融数据（非原始数据）

2. 任务类型：分类模型和集成模型评分和ROC曲线

记录7个模型（逻辑回归、SVM、决策树、随机森林、GBDT、XGBoost和LightGBM）关于accuracy、precision，recall和F1-score、auc值的评分表格，并画出ROC曲线。

3.代码及注释

## 导入包
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.model_selection import  train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_curve,auc    ## 导入评价算法
import pandas as pd
import matplotlib.pyplot as plt     ## 绘图的包

data_all = pd.read_csv('data_all.csv')    ## 导入数据
X = data_all.drop(['status'],axis=1)
y = data_all['status']
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=2018)    ## 数据分类

sc = StandardScaler()    ## 数据归一化
sc.fit(X_train) 
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

def score(y_true, y_predicet, y_predict_pro):
    acc_score = accuracy_score(y_true,y_predicet)
    pre_score = precision_score(y_true,y_predicet)
    recall = recall_score(y_true,y_predicet)
    F = f1_score(y_true,y_predicet)
    auc_score = roc_auc_score(y_true,y_predict_pro)     #AUC值
 fpr, tpr, thresholds = roc_curve(y_test,y_predict_pro)      #绘制ROC曲线
    plt.plot(fpr,tpr,'b',label='AUC = %0.4f'% auc_score)
    plt.plot([0,1],[0,1],'r--',label= 'Random guess')
    plt.legend(loc='lower right')
    plt.title('ROC')
    plt.xlabel('false positive rate')
    plt.ylabel('true positive rate')
    plt.show()
#逻辑回归
lr = LogisticRegression()
lr.fit(X_train_std,y_train)
lr_predict = lr.predict(X_test_std)
lr_predict_pro = lr.predict_proba(X_test_std)[:,1]
score(y_test,lr_predict,lr_predict_pro)

#线性SVM
svc = LinearSVC()
svc.fit(X_train_std,y_train)
svc_predict = svc.predict(X_test_std)
svc_predict_pro = svc.decision_function(X_test_std)
score(y_test,svc_predict,svc_predict_pro)

#决策树
clf = DecisionTreeClassifier()
clf.fit(X_train_std,y_train)
clf_predict = clf.predict(X_test_std)
clf_predict_proba = clf.predict_proba(X_test_std)[:,1]
score(y_test,clf_predict,clf_predict_proba)

#随机森林
rfc = RandomForestClassifier()
rfc.fit(X_train_std,y_train)
rfc_predict = rfc.predict(X_test_std)
rfc_predict_proba = rfc.predict_proba(X_test_std)[:,1]
score(y_test,rfc_predict,rfc_predict_proba)

#GBDT
gdbt = GradientBoostingClassifier()
gdbt.fit(X_train_std,y_train)
gdbt_predict = gdbt.predict(X_test_std)
gdbt_predict_proba = gdbt.predict_proba(X_test_std)[:,1]
score(y_test,gdbt_predict,gdbt_predict_proba)

#XGBoost
xgbs = XGBClassifier()
xgbs.fit(X_train_std,y_train)
xgbs_predict = xgbs.predict(X_test_std)
xgbs_predict_proba = xgbs.predict_proba(X_test_std)[:,1]
score(y_test,xgbs_predict,xgbs_predict_proba)

#LightGBM
lgbm = LGBMClassifier()
lgbm.fit(X_train_std,y_train)
lgbm_predict = lgbm.predict(X_test_std)
lgbm_predict_proba = lgbm.predict_proba(X_test_std)[:,1]
score(y_test,lgbm_predict,lr_predict_pro)