Scanner Data Product Classification
Description
BLS receives bulk data from some corporations related to the cost of goods they sell and services they provide. Consumer Price Index (CPI) staff have hand-coded a segment of the items in these data into Entry Level Item (ELI) codes. To accept and make use of these bulk data transfers at scale, BLS has begun to use machine learning to label data with ELI codes. The machine learning model takes as input word frequency counts from item descriptions. Logistic regression is then used to estimate the probability of each item being classified in each ELI category based on the word frequency categorizations. The highest probability category is selected for inclusion in the data. Any selected classifications that do not meet a certain probability threshold are flagged for human review. Benefits: real-time turnaround, productivity improvements, cost savings.
Detailed example
Entry Level Item Codes
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Efficiency of item classification efforts
Audit / financial statement impact
Used for survey processing for statistical purposes
Controls / human review
ATO: Yes; PIA: N/A
Data needed
Agency Generated; respondent-provided category and product description information