  Predictive modeling is invaded by elastic, yet complex methods such as neural
networks or ensembles (model stacking, boosting or bagging). Such methods are
usually described by a large number of parameters or hyper parameters - a price
that one needs to pay for elasticity. The very number of parameters makes
models hard to understand. This paper describes a consistent collection of
explainers for predictive models, a.k.a. black boxes. Each explainer is a
technique for exploration of a black box model. Presented approaches are
model-agnostic, what means that they extract useful information from any
predictive method despite its internal structure. Each explainer is linked with
a specific aspect of a model. Some are useful in decomposing predictions, some
serve better in understanding performance, while others are useful in
understanding importance and conditional responses of a particular variable.
Every explainer presented in this paper works for a single model or for a
collection of models. In the latter case, models can be compared against each
other. Such comparison helps to find strengths and weaknesses of different
approaches and gives additional possibilities for model validation. Presented
explainers are implemented in the DALEX package for R. They are based on a
uniform standardized grammar of model exploration which may be easily extended.
The current implementation supports the most popular frameworks for
classification and regression.

Machine Learning;Artificial Intelligence;Machine Learning;Applications