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@@ -24,13 +24,19 @@ xgb_model = XGBClassifier(
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objective='binary:logistic',
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learning_rate=0.3,
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max_depth=10,
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- eval_metric='auc'
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+ eval_metric=['error', 'logloss']
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)
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-xgb_model.fit(x_train, y_train)
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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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xgb_model.save_model('./data/ad_xgb.model')
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# 6. 测试集预测
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y_test_pre = xgb_model.predict(x_test)
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+
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+test_df = x_test.copy()
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+test_df['y'] = y_test
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+test_df['y_pre'] = y_test_pre
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+test_df.to_csv('./data/test_pre.csv', index=False)
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+
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# 7. 模型效果验证
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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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