The Univariate Flagging Algorithm (UFA): An interpretable approach for predictive modeling.

ABSTRACT: In many data classification problems, a number of methods will give similar accuracy. However, when working with people who are not experts in data science such as doctors, lawyers, and judges among others, finding interpretable algorithms can be a critical success factor. Practitioners have a deep understanding of the individual input variables but far less insight into how they interact with each other. For example, there may be ranges of an input variable for which the observed outcome is significantly more or less likely. This paper describes an algorithm for automatic detection of such thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm searches for a separation that optimizes the difference between separated areas while obtaining a high level of support. We eval

SUBMITTER: Sheth M 

PROVIDER: S-EPMC6788700 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature