title: Introducing uncertainty in predictive modeling--friend or foe?
abstract: Uncertainty was introduced to chemical descriptors of 16 publicly available data sets to various degrees and in various ways in order to investigate the effect on the predictive performance of the state-of-the-art method decision tree ensembles. A number of strategies to handle uncertainty in decision tree ensembles were evaluated. The main conclusion of the study is that uncertainty to a large extent may be introduced in chemical descriptors without impairing the predictive performance of ensembles and without the predictive performance being significantly reduced from a practical point of view. The investigation further showed that even when distributions of uncertain values were provided, the ensembles method could generate equally effective models from single-point samples from these distributions. Hence, there seems to be no advantage in using more elaborate methods for handling uncertainty in chemical descriptors when using decision tree ensembles as a modeling method for the considered types of introduced uncertainty.
authors.affiliation: AstraZeneca R&D Södertälje, Sweden. ulf.norinder@astrazeneca.com
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.forename: Henrik
authors.initials: H
authors.lastname: Boström
mesh_terms: D000465:Algorithms; D000818:Animals; D001419:Bacteria; D062313:Databases, Pharmaceutical; D003663:Decision Trees; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D016013:Likelihood Functions; D011506:Proteins / Q000737:chemistry*; D055672:Static Electricity; D013499:Surface Properties; D035501:Uncertainty; D014780:Viruses
chemical_list: D011506:Proteins