Introducing Uncertainty in Predictive ModelingFriend or Foe?

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.