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Visual Interpretation of Kernel‐Based Prediction Models
Author(s) -
Hansen Katja,
Baehrens David,
Schroeter Timon,
Rupp Matthias,
Müller KlausRobert
Publication year - 2011
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201100059
Subject(s) - interpretability , applicability domain , computer science , visualization , machine learning , artificial intelligence , interpretation (philosophy) , kernel (algebra) , reliability (semiconductor) , data mining , domain (mathematical analysis) , property (philosophy) , gaussian process , process (computing) , quantitative structure–activity relationship , gaussian , mathematics , chemistry , computational chemistry , mathematical analysis , power (physics) , philosophy , physics , epistemology , combinatorics , quantum mechanics , programming language , operating system
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.