Systems and methods for iterative feature selection for machine learning models

A method for selecting machine learning features, comprising:

• training a plurality of candidate models based on a plurality of feature groups split from a first set of features, wherein each candidate model in the plurality of candidate models takes as input a feature group from the plurality of feature groups, wherein each feature group includes no more than a first threshold number of features;
• for each candidate model in the plurality of candidate models:
• processing the candidate model to extract an explainability vector, wherein each entry in the explainability vector corresponds to a feature in the feature group associated with the candidate model and is indicative of a correlation between the feature and output of the candidate model;
• based on the explainability vector, selecting a second threshold number of features from the feature group to generate a slim feature group;
• training a slim candidate model, wherein the slim candidate model takes as input the slim feature group;
• generating a second set of features by combining features from a plurality of slim candidate models corresponding to the plurality of candidate models;
• training a final candidate model which takes as input the second set of features;
• processing the final candidate model to extract a final explainability vector;
• based on the final explainability vector, selecting a third threshold number of features from the second set of features to generate a final set of features;
• and
• training a final machine learning model which takes as input the final set of features.

One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising:

• training a plurality of candidate models based on a plurality of feature groups split from a first set of features, wherein each candidate model in the plurality of candidate models takes as input a feature group from the plurality of feature groups;
• for each candidate model in the plurality of candidate models:
• processing the candidate model to extract an explainability vector;
• based on the explainability vector, generating a slim feature group from the feature group;
• training a slim candidate model, wherein the slim candidate model takes as input the slim feature group;
• generating a second set of features by combining features from a plurality of slim candidate models corresponding to the plurality of candidate models;
• and
• training a final candidate model which takes as input the second set of features;
• processing the final candidate model to extract a final explainability vector;
• based on the final explainability vector, generating a final set of features from the second set of features;
• and
• training a final machine learning model which takes as input the final set of features.