  In recent years, machine learning researchers have focused on methods to
construct flexible and interpretable prediction models. However, an
interpretability evaluation, a relationship between generalization performance
and an interpretability of the model and a method for improving the
interpretability have to be considered. In this paper, a quantitative index of
the interpretability is proposed and its rationality is proved, and equilibrium
problem between the interpretability and the generalization performance is
analyzed. Probability upper bound of the sum of the two performances is
analyzed. For traditional supervised kernel machine learning problem, a
universal learning framework is put forward to solve the equilibrium problem
between the two performances. The condition for global optimal solution based
on the framework is deduced. The learning framework is applied to the
least-squares support vector machine and is evaluated by some experiments.
