  Providing accurate predictions is challenging for machine learning algorithms
when the number of features is larger than the number of samples in the data.
Prior knowledge can improve machine learning models by indicating relevant
variables and parameter values. Yet, this prior knowledge is often tacit and
only available from domain experts. We present a novel approach that uses
interactive visualization to elicit the tacit prior knowledge and uses it to
improve the accuracy of prediction models. The main component of our approach
is a user model that models the domain expert's knowledge of the relevance of
different features for a prediction task. In particular, based on the expert's
earlier input, the user model guides the selection of the features on which to
elicit user's knowledge next. The results of a controlled user study show that
the user model significantly improves prior knowledge elicitation and
prediction accuracy, when predicting the relative citation counts of scientific
documents in a specific domain.
