快速估算模型泛化误差，比传统方法更准更快。
RandALO: Out-of-sample risk estimation in no time flat
- 用随机近似方法替代留一法，降低计算开销。
- 在高维数据上保持估计一致，偏差低于K折交叉验证。
- 适合需要高效调参的机器学习实践者使用。
在大规模高维数据集上训练模型时，估算泛化风险是机器学习流程中必不可少但代价高昂的步骤，有助于最优地调整超参数。交叉验证（CV）虽为标准方法，却在高偏差（如K折交叉验证）与高计算成本（如留一法交叉验证）之间难以平衡。本文提出一种随机近似留一法（RandALO）风险估计器，不仅在高维情况下保持风险估计的一致性，且计算成本低于K折交叉验证。通过在合成数据和真实数据上的大量模拟实验验证了该方法的有效性，并提供了可在PyPI上获取的Python工具包randalo，GitHub地址为https://github.com/cvxgrp/randalo。
原文摘要 · Abstract (English)
Estimating out-of-sample risk for models trained on large high-dimensional datasets is an expensive but essential part of the machine learning process, enabling practitioners to optimally tune hyperparameters. Cross-validation (CV) serves as the de facto standard for risk estimation but poorly trades off high bias ($K$-fold CV) for computational cost (leave-one-out CV). We propose a randomized approximate leave-one-out (RandALO) risk estimator that is not only a consistent estimator of risk in high dimensions but also less computationally expensive than $K$-fold CV. We support our claims with extensive simulations on synthetic and real data and provide a user-friendly Python package implementing RandALO available on PyPI as randalo and at https://github.com/cvxgrp/randalo.
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