PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Visual In- terpretation of Kernel-Based Prediction Models
Katja Hansen, David Baehrens, Timon Schröter, Matthias Rupp and Klaus-Robert Müller
Molecular Informatics Volume 30, Number 9, pp. 817-826, 2011. ISSN 1868-1751

Abstract

Statistical models are frequently used to estimate molecular properties, e.g., to establish quantitative structure-activity and structure-property relationships. For such models, interpretability, knowledge of the domain of applicability, and an estimate of confidence in the predictions are essential. We develop and validate a method for the interpretation of kernel-based prediction models. As a consequence of interpretability, the method helps to assess the domain of applicability of a model, to judge the reliability of a prediction, and to determine relevant molecular features. Increased interpretability also facilitates the acceptance of such models. Our method is based on visualization: For each prediction, the most contributing training samples are computed and visualized. We quantitatively show the effectiveness of our approach by conducting a questionnaire study with 71 participants, resulting in significant improvements of the participants’ ability to distinguish between correct and incorrect predictions of a Gaussian process model for Ames mutagenicity.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:9394
Deposited By:Katja Hansen
Deposited On:16 March 2012