LGAIMLMay 5, 2016

A note on adjusting $R^2$ for using with cross-validation

arXiv:1605.01703v14 citations
Originality Synthesis-oriented
AI Analysis

This is an incremental improvement for statisticians and data scientists using cross-validation for model evaluation.

The paper addresses the problem of adjusting the coefficient of determination (R²) for use with leave-one-out cross-validation to measure predictive accuracy, providing a method to correct for bias in this context.

We show how to adjust the coefficient of determination ($R^2$) when used for measuring predictive accuracy via leave-one-out cross-validation.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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