Error-driven feature ideation in machine learning

摘要

Disclosed herein are technologies directed to a feature ideator. The feature ideator can initiate a classifier that analyzes a training set of data in a classification process. The feature ideator can generate one or more suggested features relating to errors generated during the classification process. The feature ideator can generate an output to cause the errors to be rendered in a format that provides for an interaction with a user. A user can review the summary of the errors or the individual errors and select one or more features to increase the accuracy of the classifier.