RandALO: Out-of-sample risk estimation in no time flat
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912345622577152 |
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| author | Nobel, Parth LeJeune, Daniel Candès, Emmanuel J. |
| author_facet | Nobel, Parth LeJeune, Daniel Candès, Emmanuel J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RandALO: Out-of-sample risk estimation in no time flat Nobel, Parth LeJeune, Daniel Candès, Emmanuel J. Statistics Theory Machine Learning Optimization and Control Computation 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. |
| title | RandALO: Out-of-sample risk estimation in no time flat |
| topic | Statistics Theory Machine Learning Optimization and Control Computation |
| url | https://arxiv.org/abs/2409.09781 |