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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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一个透明的机器学习算法能比黑子同行更好地预测吗? 一项使用110个数据集的基准研究.

Ryan A Peterson1, Max McGrath1, Joseph E Cavanaugh2

  • 1Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, 13001 E. 17th Pl, Aurora, CO 80045, USA.

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概括
此摘要是机器生成的。

我们开发了一个新的机器学习 (ML) 算法,使用排序稀疏性来创建透明的,人类可以理解的模型. 这种可解释的方法在许多真实世界数据集的准确性上与黑子方法相美.

关键词:
可以解释的机器学习功能选择 功能选择拉索 (Lasso) 是一个拉索.模型选择,模型选择.

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科学领域:

  • 计算机科学 计算机科学
  • 统计 统计 统计 统计
  • 人工智能的人工智能

背景情况:

  • 黑盒机器学习 (ML) 模型往往缺乏透明度,阻碍了人类的理解.
  • 现有的ML算法难以平衡模型解释性与非线性和相互作用的灵活性.

研究的目的:

  • 开发一种新的以人为中心的ML算法,产生透明的模型.
  • 为了评估这种新算法的性能与流行的黑子方法相比.

主要方法:

  • 开发了一个基于排名稀疏性的新算法,优先考虑更简单的术语而不是复杂的交互.
  • 在开源R包中实现了算法,sparseR.
  • 从宾夕法尼亚大学机器学习基准数据库的模拟和现实数据集上对其他ML方法进行了对比.

主要成果:

  • 以人为中心的算法实现了竞争力的预测准确性,与神经网络,随机森林和支持矢量机器等黑子方法竞争.
  • 在大多数现实数据集上,可解释方法的最佳性能或最佳方法的5%以内.
  • 性能与黑子方法相比,可解释方法的表现低于黑子方法的表现.

结论:

  • 新的排名稀疏性算法为黑子模型提供了可行的替代方案,提供了可解释性和竞争性准确性.
  • 应考虑以人为中心的透明算法用于预测建模应用.
  • 该sparseR包促进了可解释的ML方法的使用.