  State of the art machine learning algorithms are highly optimized to provide
the optimal prediction possible, naturally resulting in complex models. While
these models often outperform simpler more interpretable models by order of
magnitudes, in terms of understanding the way the model functions, we are often
facing a "black box".
  In this paper we suggest a simple method to interpret the behavior of any
predictive model, both for regression and classification. Given a particular
model, the information required to interpret it can be obtained by studying the
partial derivatives of the model with respect to the input. We exemplify this
insight by interpreting convolutional and multi-layer neural networks in the
field of natural language processing.
