title: Trade-off between accuracy and interpretability for predictive in silico modeling.
abstract: BACKGROUND
  Accuracy concerns the ability of a model to make correct predictions, while interpretability concerns to what degree the model allows for human understanding. Models exhibiting the former property are many times more complex and opaque, while interpretable models may lack the necessary accuracy. The trade-off between accuracy and interpretability for predictive in silico modeling is investigated.
  
  
  METHODS
  A number of state-of-the-art methods for generating accurate models are compared with state-of-the-art methods for generating transparent models.
  
  
  CONCLUSIONS
  Results on 16 biopharmaceutical classification tasks demonstrate that, although the opaque methods generally obtain higher accuracies than the transparent ones, one often only has to pay a quite limited penalty in terms of predictive performance when choosing an interpretable model.
authors.affiliation: School of Business & Informatics, University of Borås, SE-50190, Sweden. ulf.johansson@hb.se
authors.forename: Ulf
authors.initials: U
authors.lastname: Johansson
authors.forename: Cecilia
authors.initials: C
authors.lastname: Sönströd
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.forename: Henrik
authors.initials: H
authors.lastname: Boström
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D008962:Models, Theoretical; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification
chemical_list: D004364:Pharmaceutical Preparations