  A main focus of machine learning research has been improving the
generalization accuracy and efficiency of prediction models. Many models such
as SVM, random forest, and deep neural nets have been proposed and achieved
great success. However, what emerges as missing in many applications is
actionability, i.e., the ability to turn prediction results into actions. For
example, in applications such as customer relationship management, clinical
prediction, and advertisement, the users need not only accurate prediction, but
also actionable instructions which can transfer an input to a desirable goal
(e.g., higher profit repays, lower morbidity rates, higher ads hit rates).
Existing effort in deriving such actionable knowledge is few and limited to
simple action models which restricted to only change one attribute for each
action. The dilemma is that in many real applications those action models are
often more complex and harder to extract an optimal solution.
  In this paper, we propose a novel approach that achieves actionability by
combining learning with planning, two core areas of AI. In particular, we
propose a framework to extract actionable knowledge from random forest, one of
the most widely used and best off-the-shelf classifiers. We formulate the
actionability problem to a sub-optimal action planning (SOAP) problem, which is
to find a plan to alter certain features of a given input so that the random
forest would yield a desirable output, while minimizing the total costs of
actions. Technically, the SOAP problem is formulated in the SAS+ planning
formalism, and solved using a Max-SAT based approach. Our experimental results
demonstrate the effectiveness and efficiency of the proposed approach on a
personal credit dataset and other benchmarks. Our work represents a new
application of automated planning on an emerging and challenging machine
learning paradigm.
