  The goal of model distillation is to faithfully transfer teacher model
knowledge to a model which is faster, more generalizable, more interpretable,
or possesses other desirable characteristics. Human-readability is an important
and desirable standard for machine-learned model interpretability. Readable
models are transparent and can be reviewed, manipulated, and deployed like
traditional source code. As a result, such models can be improved outside the
context of machine learning and manually edited if desired. Given that directly
training such models is difficult, we propose to train interpretable models
using conventional methods, and then distill them into concise, human-readable
code.
  The proposed distillation methodology approximates a model's univariate
numerical functions with piecewise-linear curves in a localized manner. The
resulting curve model representations are accurate, concise, human-readable,
and well-regularized by construction. We describe a piecewise-linear
curve-fitting algorithm that produces high-quality results efficiently and
reliably across a broad range of use cases. We demonstrate the effectiveness of
the overall distillation technique and our curve-fitting algorithm using four
datasets across the tasks of classification, regression, and ranking.
