Systems and methods for machine learning feature generation

A non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform operations for generating a machine learning model, the operations comprising:

• generating a machine learning feature template, the machine learning feature template comprising a first grouping of first machine learning feature variables and a second grouping of second machine learning feature variables;
• generating a plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping;
• training a first machine learning model utilizing the plurality of machine learning features and first training data to generate a machine learning output;
• analyzing the machine learning output to determine a prediction accuracy of the plurality of machine learning features;
• based on the prediction accuracy of the plurality of machine learning features, selecting a subset of the plurality of machine learning features;
• training a second machine learning model based on the subset of the plurality of machine learning features and the first training data;
• and
• providing a network transaction to the second machine learning model to generate a classification of the network transaction.

A commerce platform system for generating a machine learning model, comprising:

• a memory;
• and
• a processor coupled with the memory configured to:
• generate a machine learning feature template, the machine learning feature template comprising a first grouping of first machine learning feature variables and a second grouping of second machine learning feature variables;
• generate a plurality of machine learning features by combining a respective one of each of the first machine learning feature variables of the first grouping with a respective one of each of the second machine learning feature variables of the second grouping;
• train a first machine learning model utilizing the plurality of machine learning features and first training data to generate a machine learning output;
• analyze the machine learning output to determine a prediction accuracy of the plurality of machine learning features;
• based on the prediction accuracy of the plurality of machine learning features, select a subset of the plurality of machine learning features;
• train a second machine learning model based on the subset of the plurality of machine learning features and the first training data;
• and
• provide a network transaction to the second machine learning model to generate a classification of the network transaction.