title: Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination.
abstract: Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.
authors.affiliation: H. Lundbeck A/S, Ottiliavej 9, 2500 Valby, Denmark.
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.forename: Lars
authors.initials: L
authors.lastname: Carlsson
authors.forename: Scott
authors.initials: S
authors.lastname: Boyer
authors.forename: Martin
authors.initials: M
authors.lastname: Eklund
mesh_terms: D003198:Computer Simulation; D008956:Models, Chemical; D008968:Molecular Conformation; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis