1 research outputs found
Optimal Piecewise Local-Linear Approximations
Existing works on "black-box" model interpretation use local-linear
approximations to explain the predictions made for each data instance in terms
of the importance assigned to the different features for arriving at the
prediction. These works provide instancewise explanations and thus give a local
view of the model. To be able to trust the model it is important to understand
the global model behavior and there are relatively fewer works which do the
same. Piecewise local-linear models provide a natural way to extend
local-linear models to explain the global behavior of the model. In this work,
we provide a dynamic programming based framework to obtain piecewise
approximations of the black-box model. We also provide provable fidelity, i.e.,
how well the explanations reflect the black-box model, guarantees. We carry out
simulations on synthetic and real datasets to show the utility of the proposed
approach. At the end, we show that the ideas developed for our framework can
also be used to address the problem of clustering for one-dimensional data. We
give a polynomial time algorithm and prove that it achieves optimal clustering