Exhaustive learning techniques for machine learning algorithms

Abstract

Techniques are disclosed relating to exhaustive learning techniques for machine learning algorithms. In various embodiments, the disclosed techniques include performing an iterative machine learning operation that includes training a first version of a machine learning model (e.g., a decision tree model) based on a current version of a training dataset, where the first version of the machine learning model includes a plurality of decision branches, identifying a first subset of data samples that satisfy evaluation criteria included in a first one of the plurality of decision branches, and removing this first subset of data samples to generate an updated version of the training dataset. In various embodiments, the disclosed techniques include repeating the iterative machine learning operation using the updated version of the training dataset to produce a final trained version of the machine learning model.

CPC Classifications