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Updated: May 16, 2025

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机器学习工作流程超出线性模型在低数据制度

David Dalmau1, Matthew S Sigman2, Juan V Alegre-Requena1

  • 1Departamento de Química Inorgánica, Instituto de Síntesis Química y Catálisis Homogénea (ISQCH), CSIC-Universidad de Zaragoza C/Pedro Cerbuna 12 50009 Zaragoza Spain jv.alegre@csic.es.

Chemical science
|April 17, 2025
PubMed
概括

自动非线性机器学习模型现在在数据有限的化学研究中与线性回归竞争. 这些新的工作流程提高了可解释性和预测准确性,即使是小数据集.

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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