Optimizing Approximate Leave-one-out Cross-validation to Tune Hyperparameters

• URL: http://arxiv.org/abs/2011.10218v1
• Date: Fri, 20 Nov 2020 04:57:41 GMT
• Title: Optimizing Approximate Leave-one-out Cross-validation to Tune Hyperparameters
• Authors: Ryan Burn
• Abstract summary: We derive efficient formulas to compute the hessian gradient and the gradient of ALO. We demonstrate the usefulness of the proposed approach by finding hyper parameters for regularized logistic regression and ridge regression on various real-world data sets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For a large class of regularized models, leave-one-out cross-validation can be efficiently estimated with an approximate leave-one-out formula (ALO). We consider the problem of adjusting hyperparameters so as to optimize ALO. We derive efficient formulas to compute the gradient and hessian of ALO and show how to apply a second-order optimizer to find hyperparameters. We demonstrate the usefulness of the proposed approach by finding hyperparameters for regularized logistic regression and ridge regression on various real-world data sets.

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