向后淘汰法在特征选择过程中具有多方面的优势。首先，它能显著提升模型的简洁性，通过减少特征维度使模型更易于解释和理解。其次，移除不相关特征能够潜在地提高模型性能，有效避免过拟合现象。此外，特征数量的减少还能降低计算复杂度，提高模型训练和预测的效率。

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