  In the literature, two series of models have been proposed to address
prediction problems including classification and regression. Simple models,
such as generalized linear models, have ordinary performance but strong
interpretability on a set of simple features. The other series, including
tree-based models, organize numerical, categorical and high dimensional
features into a comprehensive structure with rich interpretable information in
the data.
  In this paper, we propose a novel Discriminative Pattern-based Prediction
framework (DPPred) to accomplish the prediction tasks by taking their
advantages of both effectiveness and interpretability. Specifically, DPPred
adopts the concise discriminative patterns that are on the prefix paths from
the root to leaf nodes in the tree-based models. DPPred selects a limited
number of the useful discriminative patterns by searching for the most
effective pattern combination to fit generalized linear models. Extensive
experiments show that in many scenarios, DPPred provides competitive accuracy
with the state-of-the-art as well as the valuable interpretability for
developers and experts. In particular, taking a clinical application dataset as
a case study, our DPPred outperforms the baselines by using only 40 concise
discriminative patterns out of a potentially exponentially large set of
patterns.
