  An important feature of successful supervised machine learning applications
is to be able to explain the predictions given by the regression or
classification model being used. However, most state-of-the-art models that
have good predictive power lead to predictions that are hard to interpret.
Thus, several model-agnostic interpreters have been developed recently as a way
of explaining black-box classifiers. In practice, using these methods is a slow
process because a novel fitting is required for each new testing instance, and
several non-trivial choices must be made. We develop NLS (neural local
smoother), a method that is complex enough to give good predictions, and yet
gives solutions that are easy to be interpreted without the need of using a
separate interpreter. The key idea is to use a neural network that imposes a
local linear shape to the output layer. We show that NLS leads to predictive
power that is comparable to state-of-the-art machine learning models, and yet
is easier to interpret.
