This blog is dedicated to help agencies – who are wary of domestic and foreign abusers of the Unemployment Insurance system – by describing our successful fraud detection work, where and how it is being used, and how to leverage the lessons learned and tools built to identify FWA.

In this blog Dr. John Elder helps readers understand the flaw of using Area Under the Curve (AUC) as a metric of model performance and better ways to measure that value.

Blog highlights how Natural Language Processing (NLP) helps regulatory agencies, regulated enterprises, and markets understand unstructured regulatory documents without countless hours spent researching, reading, and analyzing. It helps analysts increase efficiency, derive actionable insights, and uncover hidden topics from large collections of rules, filings, or reports.

Dr. Jordan Barr takes explores the attributes and applications of model ensembles and potential downsides to provide context for when to use them.

This blog by Peter Bruce explores the differences between modeling for description versus for explanation and how your goals determine which method to use.

When trying to get decision-making insights from data, we often must start with helping to clean and organize the data architecture so we can build data science and machine learning models, a process called data engineering. This blog explores process of preparing data for analytical analysis.

Hyperparameters are the high-level “knobs” or “levers” of a model. In this blog Data Scientist Dr. Trent Bradberry explores hyperparameters in more detail and some ways to find good sets of them to reliably automate the model tuning process.