【Task4(2天)】 模型评估

记录5个模型（逻辑回归、SVM、决策树、随机森林、XGBoost）关于accuracy、precision，recall和F1-score、auc值的评分表格，并画出ROC曲线。时间：2天

可以参照以下格式：

 

说明：这份数据集是金融数据（非原始数据，已经处理过了），我们要做的是预测贷款用户是否会逾期。表格中 "status" 是结果标签：0表示未逾期，1表示逾期。

1.绘图绘表格函数

这里直接用的是上一篇的处理后的数据，定义好的模型

from sklearn.metrics import recall_score,precision_score,f1_score,accuracy_score,roc_curve,roc_auc_score
import numpy as np

def plot_roc_curve(fpr_train, tpr_train,fpr_test,tpr_test, name=None):
    plt.plot(fpr_train, tpr_train, linewidth=2,c='r',label='train')
    plt.plot(fpr_test, tpr_test, linewidth=2,c='b',label='test')
    plt.plot([0, 1], [0, 1], 'k--')
    plt.axis([0, 1, 0, 1])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title(name)
    plt.legend(loc='best')
    plt.show()

def metrics(models,X_train_scaled,X_test_scaled,y_train,y_test):
    results_test = pd.DataFrame(columns=['recall_score','precision_score','f1_score','accuracy_score','AUC'])
    results_train = pd.DataFrame(columns=['recall_score','precision_score','f1_score','accuracy_score','AUC'])
    for model in models:
        name = str(model)
        result_train = []
        result_test = []
        model = models[model]
        model.fit(X_train_scaled,y_train)
        y_pre_test = model.predict(X_test_scaled)
        y_pre_train = model.predict(X_train_scaled)
        result_test.append(round(recall_score(y_pre_test,y_test),2))
        result_test.append(round(precision_score(y_pre_test,y_test),2))
        result_test.append(round(f1_score(y_pre_test,y_test),2))
        result_test.append(round(accuracy_score(y_pre_test,y_test),2))
        result_test.append(round(roc_auc_score(y_pre_test,y_test),2))
        
        
        result_train.append(round(recall_score(y_pre_train,y_train),2))
        result_train.append(round(precision_score(y_pre_train,y_train),2))
        result_train.append(round(f1_score(y_pre_train,y_train),2))
        result_train.append(round(accuracy_score(y_pre_train,y_train),2))
        result_train.append(round(roc_auc_score(y_pre_train,y_train),2))
        
        fpr_train, tpr_train, thresholds_train = roc_curve(y_pre_train,y_train)
        fpr_test, tpr_test, thresholds_test = roc_curve(y_pre_test,y_test)
        plot_roc_curve(fpr_train, tpr_train,fpr_test,tpr_test,name)
        
        results_test.loc[name] = result_test
        results_train.loc[name] = result_train
    return results_test,results_train

results_test,results_train = metrics(models,X_train_scaled,X_test_scaled,y_train,y_test)

结果如下

训练集：（数模型过拟合的很厉害！！）

测试集：

模型ROC曲线：