Aggregated feature importance for finding influential business metrics

A system, comprising:

• at least one processor;
• and
• at least one non-transitory memory storing instructions to perform operations when executed by the at least one processor including:
• splitting data records into datasets, each dataset comprising a training dataset for training a machine learning model;
• generating the machine learning model using each of the datasets, the machine learning model outputting one or more predicting features having influence in predicting the response value for each of the datasets;
• determining an important feature based on the one or more predicting features;
• and
• generating report data indicating that a business metric associated with the important feature has the highest predicted influence among the features on predicting the response value.

A non-transitory computer-readable storage medium comprising instructions for determining feature importance of analytics data in predicting a response value, wherein instructions when executed by a processor perform operations comprising:

• splitting data records into datasets, each dataset comprising a training dataset for training a machine learning model;
• generating the machine learning model using each of the datasets, the machine learning model outputting one or more predicting features having influence in predicting the response value for each of the datasets;
• determining an important feature based on the one or more predicting features;
• and
• generating report data indicating that a business metric associated with the important feature has the highest predicted influence among the features on predicting the response value.