  Predictive coding, once used in only a small fraction of legal and business
matters, is now widely deployed to quickly cull through increasingly vast
amounts of data and reduce the need for costly and inefficient human document
review. Previously, the sole front-end input used to create a predictive model
was the exemplar documents (training data) chosen by subject-matter experts.
Many predictive coding tools require users to rely on static preprocessing
parameters and a single machine learning algorithm to develop the predictive
model. Little research has been published discussing the impact preprocessing
parameters and learning algorithms have on the effectiveness of the technology.
A deeper dive into the generation of a predictive model shows that the settings
and algorithm can have a strong effect on the accuracy and efficacy of a
predictive coding tool. Understanding how these input parameters affect the
output will empower legal teams with the information they need to implement
predictive coding as efficiently and effectively as possible. This paper
outlines different preprocessing parameters and algorithms as applied to
multiple real-world data sets to understand the influence of various
approaches.
