提出精确计算Lasso模型留一法交叉验证的算法，提升超参数优化效率。
Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso
- 构建分段二次函数算法，精确计算留一法交叉验证
- 可全局或局部寻找最优超参数，实测数据集表现良好
- 支持近似计算，适用于更大规模数据集
本文提出一种算法，可生成分段二次函数，以精确计算Lasso模型在不同超参数下的留一法交叉验证结果。该算法可用于全局或局部搜索最优超参数，并在真实数据集上验证了其实际可行性。同时，算法还可调整为近似版本，以适应更大规模数据集的计算需求。
原文摘要 · Abstract (English)
I develop an algorithm to produce the piecewise quadratic that computes leave-one-out cross-validation for the lasso as a function of its hyperparameter. The algorithm can be used to find exact hyperparameters that optimize leave-one-out cross-validation either globally or locally, and its practicality is demonstrated on real-world data sets. I also show how the algorithm can be modified to compute approximate leave-one-out cross-validation, making it suitable for larger data sets.
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