Systems and methods for an adaptive sampling of unlabeled data samples for constructing an informative training data corpus that improves a training and predictive accuracy of a machine learning model

Abstract

A system and method for adaptively sampling a corpus of data samples for improving an accuracy of a predictive machine learning model includes: identifying the corpus of data samples, wherein each data sample of the corpus of data samples is associated with a machine learning-derived threat inference value; stratifying the corpus of data samples into a plurality of distinct strata based on the machine learning-derived threat inference value associated with each data sample of the corpus of data samples; adaptively sampling the plurality of distinct strata; constructing a machine learning training corpus comprising a plurality of data samples based on the adaptive sampling of the plurality of distinct strata; and training the predictive machine learning model based on the machine learning training corpus.