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@@ -33,7 +33,7 @@ xgb_model = XGBClassifier(
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objective='binary:logistic',
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learning_rate=0.3,
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max_depth=5,
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- eval_metric=['error', 'logloss', 'auc']
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+ eval_metric=['mae', 'auc']
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)
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xgb_model.fit(x_train, y_train, eval_set=[(x_train, y_train), (x_test, y_test)])
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# 5. 模型保存
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@@ -51,3 +51,9 @@ test_accuracy = metrics.accuracy_score(y_test, y_test_pre)
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print("Test Accuracy: %.2f%%" % (test_accuracy * 100.0))
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test_auc = metrics.roc_auc_score(y_test, y_test_pre)
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print("auc: %.2f%%" % (test_auc * 100.0))
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+test_recall = metrics.recall_score(y_test, y_test_pre)
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+print("recall:%.2f%%"%(test_recall*100.0))
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+test_f1 = metrics.f1_score(y_test, y_test_pre)
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+print("f1:%.2f%%"%(test_f1*100.0))
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+test_precision = metrics.precision_score(y_test, y_test_pre)
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+print("precision:%.2f%%"%(test_precision*100.0))
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