【Task4(2天)】 模型評估
阿新 • • 發佈:2019-05-17
時間 cal wid boost ive div learn col 決策
記錄5個模型(邏輯回歸、SVM、決策樹、隨機森林、XGBoost)關於accuracy、precision,recall和F1-score、auc值的評分表格,並畫出ROC曲線。時間:2天
可以參照以下格式:
說明:這份數據集是金融數據(非原始數據,已經處理過了),我們要做的是預測貸款用戶是否會逾期。表格中 "status" 是結果標簽:0表示未逾期,1表示逾期。
1.繪圖繪表格函數
這裏直接用的是上一篇的處理後的數據,定義好的模型
from sklearn.metrics import recall_score,precision_score,f1_score,accuracy_score,roc_curve,roc_auc_scoreimport 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)
結果如下
訓練集:(數模型過擬合的很厲害!!)
測試集:
模型ROC曲線:
【Task4(2天)】 模型評估