SYSTEMS AND METHODS FOR DETECTING AND CORRECTING DRIFT IN A DATA SET

Abstract

Embodiments of the present disclosure include techniques for detecting and correcting drift in a data set. Data sets may be divided into classifications. A first classifier is trained on data from multiple data sets using data from each data set having a first classification. A second classifier is trained on data from the multiple data sets using data from each data set having a second classification. The performance of the classifiers are measured. Drift is detected when the performance of either classifier is above a threshold. Some embodiments may use the trained classifiers to determine data elements from one data set that are combined with another data set for training.

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