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Data · dataset · 2026

<p>Comparison of now-casting model performance and bias-variance trade-off.</p>

Listed in DMU Figshare and UCL Research Data Repository — shown once because both records carry DOI 10.1371/journal.pone.0358063.g002

<p>Predictive performance of Linear Regression (circles) and Random Forest (squares) using different search-term clusters (All, C1, C2, C3) during the 2009 Flu and COVID-19 pandemics.

Description

Panels a), b), and d) correspond to flu, while c) and e) to COVID-19. For flu, error bars represent standard deviation across multiple seasons (P1–P6), reflecting model variability.

Panel a) highlights C1’s superior performance over “All”. For COVID-19, standard deviations are not shown due to the rolling window approach, which results in overlapping training data and non-independent <i>R</i><sup>2</sup> estimates. Instead, panel c) presents the <i>R</i><sup>2</sup> of the mean prediction across models.

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Panels d) and e) illustrate the bias-variance trade-off. Random Forest has lower variance but slightly higher bias than Linear Regression. In flu (d), C1 reduces variance compared to “All”, suggesting that selecting fewer, more relevant terms improves stability.

In COVID-19 (e), bias remains high due to the pandemic’s unpredictability. While here C1 also lowers variance relative to “All”, this effect is weaker than in flu, where seasonal patterns allow for more consistent predictions. LASSO shows high bias and low variance.</p>

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From keywords
Cancer · Cancer · Earth & Environmental Science · Earth & Environmental Science · Infectious diseases · Infectious diseases · Life Sciences · Life Sciences · Mathematics & Statistics · Mathematics & Statistics · Medicine & Health · Medicine & Health
Provenance · 2 source records, 17 field assertions
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DMU Figshareoai:figshare.com:article/3404843813 d agoJSON v1
UCL Research Data Repositoryoai:figshare.com:article/3404843813 d agoJSON v1
FieldAssertionExtractorEvidence
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