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.

翻译：本文提出一种算法，用于生成分段二次函数，该函数将Lasso留一交叉验证计算为其超参数的函数。该算法可用于全局或局部地寻找优化留一交叉验证的精确超参数，并通过实际数据集验证了其实用性。同时，本文展示了如何修改该算法以计算近似留一交叉验证，使其适用于更大规模的数据集。