对比三种特征选择方法对XGBoost模型性能影响，发现不影响准确率。
The effect of different feature selection methods on models created with XGBoost
- 测试三种特征降维方法在XGBoost中的表现
- 三类方法预测准确率差异无统计显著性
- 适合关注计算效率而非精度的场景
本研究探讨不同特征选择方法对使用XGBoost构建的模型的影响。XGBoost是一种具有优异正则化能力的流行机器学习算法。研究发现，三种不同的特征维度缩减方式对模型预测准确率均未产生统计上显著的影响。这表明，传统上通过去除噪声数据防止过拟合的做法，在XGBoost中可能并不适用。但该做法仍可用于降低计算复杂度。
原文摘要 · Abstract (English)
This study examines the effect that different feature selection methods have on models created with XGBoost, a popular machine learning algorithm with superb regularization methods. It shows that three different ways for reducing the dimensionality of features produces no statistically significant change in the prediction accuracy of the model. This suggests that the traditional idea of removing the noisy training data to make sure models do not overfit may not apply to XGBoost. But it may still be viable in order to reduce computational complexity.
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