title: Visual Interpretation of Kernel-Based Prediction Models.
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.
authors.affiliation: Machine Learning Group, Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587 Berlin, Germany phone: 0049 30 31 4 24927. katja.hansen@tu-berlin.de.
authors.forename: Katja
authors.initials: K
authors.lastname: Hansen
authors.affiliation: Machine Learning Group, Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587 Berlin, Germany phone: 0049 30 31 4 24927.
authors.forename: David
authors.initials: D
authors.lastname: Baehrens
authors.affiliation: Market Valid GmbH & Co KG, Große Hamburger Str. 16, 10115 Berlin, Germany.
authors.forename: Timon
authors.initials: T
authors.lastname: Schroeter
authors.affiliation: Machine Learning Group, Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587 Berlin, Germany phone: 0049 30 31 4 24927.|Institute for Pure and Applied Mathematics, UCLA, Los Angeles, CA 90095-7121, USA.
authors.forename: Matthias
authors.initials: M
authors.lastname: Rupp
authors.affiliation: Machine Learning Group, Technische Universität Berlin, Franklinstr. 28/29, FR 6-9, 10587 Berlin, Germany phone: 0049 30 31 4 24927.|Institute for Pure and Applied Mathematics, UCLA, Los Angeles, CA 90095-7121, USA.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
keywords: Confidence estimation; Domain of applicability; Kernel-based learning; QSAR; QSPR