任务1.2 - 模型构建之集成模型,预测贷款用户是否会逾期

【任务1.2 - 模型构建之集成模型】

说明：对上一篇Task1.1模型构建,预测贷款用户是否会逾期代码的补充，需在上一篇代码之后运行

构建随机森林、GBDT、XGBoost和LightGBM这4个模型，并对每一个模型进行评分，评分方式任意，例如准确度和auc值。

关键点：4个模型和对应的评分结果
石墨链接 https://shimo.im/docs/jse5ZZhdvEQR4siC

模型构建

#导入库
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
import matplotlib.pyplot as plt

#4个模型
rf = RandomForestClassifier().fit(x_train, y_train)
GBDT = GradientBoostingClassifier().fit(x_train, y_train)
XGBoost = XGBClassifier().fit(x_train, y_train)
LightGBM = LGBMClassifier().fit(x_train, y_train)

4个模型对应的评分

#放到dict里，后面直接for循环
models = {'RF': rf,
'GBDT': GBDT,
'XGBoost': XGBoost,
'LightGBM': LightGBM}

df_result = pd.DataFrame(columns=('Model', 'Accuracy', 'Precision', 'Recall', 'F1 score', 'AUC','AUC1'))
row = 0

for name,clf in models.items():
y_test_pred = clf.predict(x_test)

acc = metrics.accuracy_score(y_test, y_test_pred)
p = metrics.precision_score(y_test, y_test_pred)
r = metrics.recall_score(y_test, y_test_pred)
f1 = metrics.f1_score(y_test, y_test_pred)

y_test_proba = clf.predict_proba(x_test)
fpr, tpr, thresholds = metrics.roc_curve(y_test, y_test_proba[:, 1])
auc = metrics.auc(fpr, tpr)
auc1 = metrics.roc_auc_score(y_test, y_test_proba[:, 1])  # auc求法2

df_result.loc[row] = [name, acc, p, r, f1, auc,auc1]
row += 1
print(df_result)

Model  Accuracy  Precision    Recall  F1 score       AUC      AUC1
0        RF  0.770848   0.601266  0.264624  0.367505  0.718992  0.718992
1      GBDT  0.780659   0.611650  0.350975  0.446018  0.763828  0.763828
2  LightGBM  0.770147   0.570136  0.350975  0.434483  0.757402  0.757402
3   XGBoost  0.785564   0.630542  0.356546  0.455516  0.771363  0.771363

4个模型的ROC曲线

def plot_roc_curve(fpr, tpr, label=None):
plt.plot(fpr, tpr, label=label)

plt.figure()
for name, model in models.items():
proba = model.predict_proba(x_test)[:,1]
fpr, tpr, thresholds = metrics.roc_curve(y_test, proba)
plot_roc_curve(fpr, tpr, label=name)

plt.plot([0, 1], [0, 1], 'k--')# 对角线
plt.axis([0, 1, 0, 1])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()