How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars

Anton Schwaighofer Timon Schroeter Sebastian Mika Gilles Blanchard

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How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars

机译：我们怎么会出错？机器学习方法和错误条评述

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large number of different machine learning methods can potentially be used for ligand-based virtual screening. In our contribution, we focus on three specific nonlinear methods, namely support vector regression, Gaussian process models, and decision trees. For each of these methods, we provide a short and intuitive introduction. In particular, we will also discuss how confidence estimates (error bars) can be obtained from these methods. We continue with important aspects for model building and evaluation, such as methodologies for model selection, evaluation, performance criteria, and how the quality of error bar estimates can be verified. Besides an introduction to the respective methods, we will also point to available implementations, and discuss important issues for the practical application.

机译：大量不同的机器学习方法可以潜在地用于基于配体的虚拟筛选。在我们的贡献中，我们专注于三种特定的非线性方法，即支持向量回归，高斯过程模型和决策树。对于每种方法，我们都提供了简短而直观的介绍。特别是，我们还将讨论如何从这些方法中获得置信度估计（误差线）。我们继续进行模型构建和评估的重要方面，例如模型选择，评估，性能标准的方法，以及如何验证误差线估计的质量。除了介绍相应方法外，我们还将指向可用的实现，并讨论实际应用中的重要问题。

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来源
《Combinatorial Chemistry & High Throughput Screening》 |2009年第5期|p.453-468|共16页
作者
Anton Schwaighofer Timon Schroeter Sebastian Mika Gilles Blanchard;
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作者单位

Technische Universität Berlin, Department of Computer Science, Franklinstraße 28/29, D-10587 Berlin, Germany.;

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正文语种 eng
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关键词
Machine learning, error bars, model building, parameter estimation, decision tree, support vector machine, Gaussian process;

机译：机器学习;误差条;模型构建;参数估计;决策树;支持向量机;高斯过程;

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How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars

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