Method and Systems for Conditioning Data Sets for Efficient Computational Processing

A method for generating a machine learning model, the method comprising:

• sampling at least one interaction effect structure of at least one multivariable dataset;
• sampling at least one hybrid variable for each sampled interaction effect structure;
• calculating a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria;
• labeling each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria;
• training a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, the training being performed using the labeled sampled hybrid variables;
• applying the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria;
• and
• retaining only hybrid variables with a likelihood value that exceeds a decision criteria.

A system for selecting hybrid variables, the system comprising:

• a processor;
• memory storing program code that is accessible and executable by the processor;
• and
• wherein, when the processor executed the program code, the processor is caused to:
• sample at least one interaction effect structure of at least one multivariable dataset;
• sample at least one hybrid variable for each sampled interaction effect structure;
• calculate a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria;
• label each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria;
• train a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, the training being performed using the labeled sampled hybrid variables;
• apply the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria;
• and
• retain only hybrid variables with a likelihood value that exceeds a decision criteria.

A computer readable medium storing non-transitory instructions which, when executed by a processor, cause the processor to:

• sample at least one interaction effect structure of at least one multivariable dataset;
• sample at least one hybrid variable for each sampled interaction effect structure;
• calculate a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria;
• label each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria;
• train a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, the training being performed using the labeled sampled hybrid variables;
• apply the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria;
• and
• retain only hybrid variables with a likelihood value that exceeds a decision criteria.