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

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

Volume: 12 Issue: 5

Author(s): Anton Schwaighofer, Timon Schroeter, Sebastian Mika and Gilles Blanchard

Affiliation:

Keywords: Machine learning, error bars, model building, parameter estimation, decision tree, support vector machine, Gaussian process

Abstract: A 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.

Cite this article as:

Schwaighofer Anton, Schroeter Timon, Mika Sebastian and Blanchard Gilles, How Wrong Can We Get? A Review of Machine Learning Approaches and Error Bars, Combinatorial Chemistry & High Throughput Screening 2009; 12 (5) . https://dx.doi.org/10.2174/138620709788489064