Abstract: A technology for determining accuracy estimates for classifications used in an electronic catalog. In one example, classifications for product groupings included in an electronic catalog may be updated as a result of the classifications inaccurately representing products included in the product groupings. The electronic catalog of products may be grouped into a plurality of product groupings using classifications. Classifications of product groupings that inaccurately represent products included in the product grouping may be updated with suggested classifications. Update metrics for updates made to the grouping classifications may be collected and the update metrics may be used to calculate an accuracy estimate for the classifications used in the electronic catalog.

Abstract: Techniques are provided herein for utilizing a classification engine to improve a classification model. For example, a classification engine may derive a statistical model based at least in part on a synthetic data set. A misclassification may be determined based at least in part on an output of the statistical model. An audit question may be provided to an individual, the audit question being determined based at least in part on the determined misclassification. Response data related to the audit question may be received. The statistical model may be validated based at least in part on the response data.