Iterative vectoring for constructing data driven machine learning models

Abstract

Embodiments described herein are directed to generating a machine learning (ML) model. A plurality of vectors are accessed, each vector of the plurality of vectors including a first set of features associated with a corresponding data item. A second set of features is identified by expanding the first set of features. A ML model is trained using vectors including the expanded set of features, and it is determined that an accuracy of the ML model trained using the vectors increased. A third set of features is identified by determining a measure of importance for different subsets of features in the second set and replacing subsets having a low measure of importance with new features. A ML model is trained using vectors that include the third set, and it is determined that an accuracy of the model increased due to the replacing.